research about blended learning model

• Research article
• Open access
• Published: 06 February 2017

Blended learning effectiveness: the relationship between student characteristics, design features and outcomes

• Mugenyi Justice Kintu   ORCID: orcid.org/0000-0002-4500-1168 1 , 2 ,
• Chang Zhu 2 &
• Edmond Kagambe 1  

International Journal of Educational Technology in Higher Education volume  14 , Article number:  7 ( 2017 ) Cite this article

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This paper investigates the effectiveness of a blended learning environment through analyzing the relationship between student characteristics/background, design features and learning outcomes. It is aimed at determining the significant predictors of blended learning effectiveness taking student characteristics/background and design features as independent variables and learning outcomes as dependent variables. A survey was administered to 238 respondents to gather data on student characteristics/background, design features and learning outcomes. The final semester evaluation results were used as a measure for performance as an outcome. We applied the online self regulatory learning questionnaire for data on learner self regulation, the intrinsic motivation inventory for data on intrinsic motivation and other self-developed instruments for measuring the other constructs. Multiple regression analysis results showed that blended learning design features (technology quality, online tools and face-to-face support) and student characteristics (attitudes and self-regulation) predicted student satisfaction as an outcome. The results indicate that some of the student characteristics/backgrounds and design features are significant predictors for student learning outcomes in blended learning.

Introduction

The teaching and learning environment is embracing a number of innovations and some of these involve the use of technology through blended learning. This innovative pedagogical approach has been embraced rapidly though it goes through a process. The introduction of blended learning (combination of face-to-face and online teaching and learning) initiatives is part of these innovations but its uptake, especially in the developing world faces challenges for it to be an effective innovation in teaching and learning. Blended learning effectiveness has quite a number of underlying factors that pose challenges. One big challenge is about how users can successfully use the technology and ensuring participants’ commitment given the individual learner characteristics and encounters with technology (Hofmann, 2014 ). Hofmann adds that users getting into difficulties with technology may result into abandoning the learning and eventual failure of technological applications. In a report by Oxford Group ( 2013 ), some learners (16%) had negative attitudes to blended learning while 26% were concerned that learners would not complete study in blended learning. Learners are important partners in any learning process and therefore, their backgrounds and characteristics affect their ability to effectively carry on with learning and being in blended learning, the design tools to be used may impinge on the effectiveness in their learning.

This study tackles blended learning effectiveness which has been investigated in previous studies considering grades, course completion, retention and graduation rates but no studies regarding effectiveness in view of learner characteristics/background, design features and outcomes have been done in the Ugandan university context. No studies have also been done on how the characteristics of learners and design features are predictors of outcomes in the context of a planning evaluation research (Guskey, 2000 ) to establish the effectiveness of blended learning. Guskey ( 2000 ) noted that planning evaluation fits in well since it occurs before the implementation of any innovation as well as allowing planners to determine the needs, considering participant characteristics, analyzing contextual matters and gathering baseline information. This study is done in the context of a plan to undertake innovative pedagogy involving use of a learning management system (moodle) for the first time in teaching and learning in a Ugandan university. The learner characteristics/backgrounds being investigated for blended learning effectiveness include self-regulation, computer competence, workload management, social and family support, attitude to blended learning, gender and age. We investigate the blended learning design features of learner interactions, face-to-face support, learning management system tools and technology quality while the outcomes considered include satisfaction, performance, intrinsic motivation and knowledge construction. Establishing the significant predictors of outcomes in blended learning will help to inform planners of such learning environments in order to put in place necessary groundwork preparations for designing blended learning as an innovative pedagogical approach.

Kenney and Newcombe ( 2011 ) did their comparison to establish effectiveness in view of grades and found that blended learning had higher average score than the non-blended learning environment. Garrison and Kanuka ( 2004 ) examined the transformative potential of blended learning and reported an increase in course completion rates, improved retention and increased student satisfaction. Comparisons between blended learning environments have been done to establish the disparity between academic achievement, grade dispersions and gender performance differences and no significant differences were found between the groups (Demirkol & Kazu, 2014 ).

However, blended learning effectiveness may be dependent on many other factors and among them student characteristics, design features and learning outcomes. Research shows that the failure of learners to continue their online education in some cases has been due to family support or increased workload leading to learner dropout (Park & Choi, 2009 ) as well as little time for study. Additionally, it is dependent on learner interactions with instructors since failure to continue with online learning is attributed to this. In Greer, Hudson & Paugh’s study as cited in Park and Choi ( 2009 ), family and peer support for learners is important for success in online and face-to-face learning. Support is needed for learners from all areas in web-based courses and this may be from family, friends, co-workers as well as peers in class. Greer, Hudson and Paugh further noted that peer encouragement assisted new learners in computer use and applications. The authors also show that learners need time budgeting, appropriate technology tools and support from friends and family in web-based courses. Peer support is required by learners who have no or little knowledge of technology, especially computers, to help them overcome fears. Park and Choi, ( 2009 ) showed that organizational support significantly predicts learners’ stay and success in online courses because employers at times are willing to reduce learners’ workload during study as well as supervisors showing that they are interested in job-related learning for employees to advance and improve their skills.

The study by Kintu and Zhu ( 2016 ) investigated the possibility of blended learning in a Ugandan University and examined whether student characteristics (such as self-regulation, attitudes towards blended learning, computer competence) and student background (such as family support, social support and management of workload) were significant factors in learner outcomes (such as motivation, satisfaction, knowledge construction and performance). The characteristics and background factors were studied along with blended learning design features such as technology quality, learner interactions, and Moodle with its tools and resources. The findings from that study indicated that learner attitudes towards blended learning were significant factors to learner satisfaction and motivation while workload management was a significant factor to learner satisfaction and knowledge construction. Among the blended learning design features, only learner interaction was a significant factor to learner satisfaction and knowledge construction.

The focus of the present study is on examining the effectiveness of blended learning taking into consideration learner characteristics/background, blended learning design elements and learning outcomes and how the former are significant predictors of blended learning effectiveness.

Studies like that of Morris and Lim ( 2009 ) have investigated learner and instructional factors influencing learning outcomes in blended learning. They however do not deal with such variables in the contexts of blended learning design as an aspect of innovative pedagogy involving the use of technology in education. Apart from the learner variables such as gender, age, experience, study time as tackled before, this study considers social and background aspects of the learners such as family and social support, self-regulation, attitudes towards blended learning and management of workload to find out their relationship to blended learning effectiveness. Identifying the various types of learner variables with regard to their relationship to blended learning effectiveness is important in this study as we embark on innovative pedagogy with technology in teaching and learning.

Literature review

This review presents research about blended learning effectiveness from the perspective of learner characteristics/background, design features and learning outcomes. It also gives the factors that are considered to be significant for blended learning effectiveness. The selected elements are as a result of the researcher’s experiences at a Ugandan university where student learning faces challenges with regard to learner characteristics and blended learning features in adopting the use of technology in teaching and learning. We have made use of Loukis, Georgiou, and Pazalo ( 2007 ) value flow model for evaluating an e-learning and blended learning service specifically considering the effectiveness evaluation layer. This evaluates the extent of an e-learning system usage and the educational effectiveness. In addition, studies by Leidner, Jarvenpaa, Dillon and Gunawardena as cited in Selim ( 2007 ) have noted three main factors that affect e-learning and blended learning effectiveness as instructor characteristics, technology and student characteristics. Heinich, Molenda, Russell, and Smaldino ( 2001 ) showed the need for examining learner characteristics for effective instructional technology use and showed that user characteristics do impact on behavioral intention to use technology. Research has dealt with learner characteristics that contribute to learner performance outcomes. They have dealt with emotional intelligence, resilience, personality type and success in an online learning context (Berenson, Boyles, & Weaver, 2008 ). Dealing with the characteristics identified in this study will give another dimension, especially for blended learning in learning environment designs and add to specific debate on learning using technology. Lin and Vassar, ( 2009 ) indicated that learner success is dependent on ability to cope with technical difficulty as well as technical skills in computer operations and internet navigation. This justifies our approach in dealing with the design features of blended learning in this study.

Learner characteristics/background and blended learning effectiveness

Studies indicate that student characteristics such as gender play significant roles in academic achievement (Oxford Group, 2013 ), but no study examines performance of male and female as an important factor in blended learning effectiveness. It has again been noted that the success of e- and blended learning is highly dependent on experience in internet and computer applications (Picciano & Seaman, 2007 ). Rigorous discovery of such competences can finally lead to a confirmation of high possibilities of establishing blended learning. Research agrees that the success of e-learning and blended learning can largely depend on students as well as teachers gaining confidence and capability to participate in blended learning (Hadad, 2007 ). Shraim and Khlaif ( 2010 ) note in their research that 75% of students and 72% of teachers were lacking in skills to utilize ICT based learning components due to insufficient skills and experience in computer and internet applications and this may lead to failure in e-learning and blended learning. It is therefore pertinent that since the use of blended learning applies high usage of computers, computer competence is necessary (Abubakar & Adetimirin, 2015 ) to avoid failure in applying technology in education for learning effectiveness. Rovai, ( 2003 ) noted that learners’ computer literacy and time management are crucial in distance learning contexts and concluded that such factors are meaningful in online classes. This is supported by Selim ( 2007 ) that learners need to posses time management skills and computer skills necessary for effectiveness in e- learning and blended learning. Self-regulatory skills of time management lead to better performance and learners’ ability to structure the physical learning environment leads to efficiency in e-learning and blended learning environments. Learners need to seek helpful assistance from peers and teachers through chats, email and face-to-face meetings for effectiveness (Lynch & Dembo, 2004 ). Factors such as learners’ hours of employment and family responsibilities are known to impede learners’ process of learning, blended learning inclusive (Cohen, Stage, Hammack, & Marcus, 2012 ). It was also noted that a common factor in failure and learner drop-out is the time conflict which is compounded by issues of family , employment status as well as management support (Packham, Jones, Miller, & Thomas, 2004 ). A study by Thompson ( 2004 ) shows that work, family, insufficient time and study load made learners withdraw from online courses.

Learner attitudes to blended learning can result in its effectiveness and these shape behavioral intentions which usually lead to persistence in a learning environment, blended inclusive. Selim, ( 2007 ) noted that the learners’ attitude towards e-learning and blended learning are success factors for these learning environments. Learner performance by age and gender in e-learning and blended learning has been found to indicate no significant differences between male and female learners and different age groups (i.e. young, middle-aged and old above 45 years) (Coldwell, Craig, Paterson, & Mustard, 2008 ). This implies that the potential for blended learning to be effective exists and is unhampered by gender or age differences.

Blended learning design features

The design features under study here include interactions, technology with its quality, face-to-face support and learning management system tools and resources.

Research shows that absence of learner interaction causes failure and eventual drop-out in online courses (Willging & Johnson, 2009 ) and the lack of learner connectedness was noted as an internal factor leading to learner drop-out in online courses (Zielinski, 2000 ). It was also noted that learners may not continue in e- and blended learning if they are unable to make friends thereby being disconnected and developing feelings of isolation during their blended learning experiences (Willging & Johnson, 2009). Learners’ Interactions with teachers and peers can make blended learning effective as its absence makes learners withdraw (Astleitner, 2000 ). Loukis, Georgious and Pazalo (2007) noted that learners’ measuring of a system’s quality, reliability and ease of use leads to learning efficiency and can be so in blended learning. Learner success in blended learning may substantially be affected by system functionality (Pituch & Lee, 2006 ) and may lead to failure of such learning initiatives (Shrain, 2012 ). It is therefore important to examine technology quality for ensuring learning effectiveness in blended learning. Tselios, Daskalakis, and Papadopoulou ( 2011 ) investigated learner perceptions after a learning management system use and found out that the actual system use determines the usefulness among users. It is again noted that a system with poor response time cannot be taken to be useful for e-learning and blended learning especially in cases of limited bandwidth (Anderson, 2004 ). In this study, we investigate the use of Moodle and its tools as a function of potential effectiveness of blended learning.

The quality of learning management system content for learners can be a predictor of good performance in e-and blended learning environments and can lead to learner satisfaction. On the whole, poor quality technology yields no satisfaction by users and therefore the quality of technology significantly affects satisfaction (Piccoli, Ahmad, & Ives, 2001 ). Continued navigation through a learning management system increases use and is an indicator of success in blended learning (Delone & McLean, 2003 ). The efficient use of learning management system and its tools improves learning outcomes in e-learning and blended learning environments.

It is noted that learner satisfaction with a learning management system can be an antecedent factor for blended learning effectiveness. Goyal and Tambe ( 2015 ) noted that learners showed an appreciation to Moodle’s contribution in their learning. They showed positivity with it as it improved their understanding of course material (Ahmad & Al-Khanjari, 2011 ). The study by Goyal and Tambe ( 2015 ) used descriptive statistics to indicate improved learning by use of uploaded syllabus and session plans on Moodle. Improved learning is also noted through sharing study material, submitting assignments and using the calendar. Learners in the study found Moodle to be an effective educational tool.

In blended learning set ups, face-to-face experiences form part of the blend and learner positive attitudes to such sessions could mean blended learning effectiveness. A study by Marriot, Marriot, and Selwyn ( 2004 ) showed learners expressing their preference for face-to-face due to its facilitation of social interaction and communication skills acquired from classroom environment. Their preference for the online session was only in as far as it complemented the traditional face-to-face learning. Learners in a study by Osgerby ( 2013 ) had positive perceptions of blended learning but preferred face-to-face with its step-by-stem instruction. Beard, Harper and Riley ( 2004 ) shows that some learners are successful while in a personal interaction with teachers and peers thus prefer face-to-face in the blend. Beard however dealt with a comparison between online and on-campus learning while our study combines both, singling out the face-to-face part of the blend. The advantage found by Beard is all the same relevant here because learners in blended learning express attitude to both online and face-to-face for an effective blend. Researchers indicate that teacher presence in face-to-face sessions lessens psychological distance between them and the learners and leads to greater learning. This is because there are verbal aspects like giving praise, soliciting for viewpoints, humor, etc and non-verbal expressions like eye contact, facial expressions, gestures, etc which make teachers to be closer to learners psychologically (Kelley & Gorham, 2009 ).

Learner outcomes

The outcomes under scrutiny in this study include performance, motivation, satisfaction and knowledge construction. Motivation is seen here as an outcome because, much as cognitive factors such as course grades are used in measuring learning outcomes, affective factors like intrinsic motivation may also be used to indicate outcomes of learning (Kuo, Walker, Belland, & Schroder, 2013 ). Research shows that high motivation among online learners leads to persistence in their courses (Menager-Beeley, 2004 ). Sankaran and Bui ( 2001 ) indicated that less motivated learners performed poorly in knowledge tests while those with high learning motivation demonstrate high performance in academics (Green, Nelson, Martin, & Marsh, 2006 ). Lim and Kim, ( 2003 ) indicated that learner interest as a motivation factor promotes learner involvement in learning and this could lead to learning effectiveness in blended learning.

Learner satisfaction was noted as a strong factor for effectiveness of blended and online courses (Wilging & Johnson, 2009) and dissatisfaction may result from learners’ incompetence in the use of the learning management system as an effective learning tool since, as Islam ( 2014 ) puts it, users may be dissatisfied with an information system due to ease of use. A lack of prompt feedback for learners from course instructors was found to cause dissatisfaction in an online graduate course. In addition, dissatisfaction resulted from technical difficulties as well as ambiguous course instruction Hara and Kling ( 2001 ). These factors, once addressed, can lead to learner satisfaction in e-learning and blended learning and eventual effectiveness. A study by Blocker and Tucker ( 2001 ) also showed that learners had difficulties with technology and inadequate group participation by peers leading to dissatisfaction within these design features. Student-teacher interactions are known to bring satisfaction within online courses. Study results by Swan ( 2001 ) indicated that student-teacher interaction strongly related with student satisfaction and high learner-learner interaction resulted in higher levels of course satisfaction. Descriptive results by Naaj, Nachouki, and Ankit ( 2012 ) showed that learners were satisfied with technology which was a video-conferencing component of blended learning with a mean of 3.7. The same study indicated student satisfaction with instructors at a mean of 3.8. Askar and Altun, ( 2008 ) found that learners were satisfied with face-to-face sessions of the blend with t-tests and ANOVA results indicating female scores as higher than for males in the satisfaction with face-to-face environment of the blended learning.

Studies comparing blended learning with traditional face-to-face have indicated that learners perform equally well in blended learning and their performance is unaffected by the delivery method (Kwak, Menezes, & Sherwood, 2013 ). In another study, learning experience and performance are known to improve when traditional course delivery is integrated with online learning (Stacey & Gerbic, 2007 ). Such improvement as noted may be an indicator of blended learning effectiveness. Our study however, delves into improved performance but seeks to establish the potential of blended learning effectiveness by considering grades obtained in a blended learning experiment. Score 50 and above is considered a pass in this study’s setting and learners scoring this and above will be considered to have passed. This will make our conclusions about the potential of blended learning effectiveness.

Regarding knowledge construction, it has been noted that effective learning occurs where learners are actively involved (Nurmela, Palonen, Lehtinen & Hakkarainen, 2003 , cited in Zhu, 2012 ) and this may be an indicator of learning environment effectiveness. Effective blended learning would require that learners are able to initiate, discover and accomplish the processes of knowledge construction as antecedents of blended learning effectiveness. A study by Rahman, Yasin and Jusoff ( 2011 ) indicated that learners were able to use some steps to construct meaning through an online discussion process through assignments given. In the process of giving and receiving among themselves, the authors noted that learners learned by writing what they understood. From our perspective, this can be considered to be accomplishment in the knowledge construction process. Their study further shows that learners construct meaning individually from assignments and this stage is referred to as pre-construction which for our study, is an aspect of discovery in the knowledge construction process.

Predictors of blended learning effectiveness

Researchers have dealt with success factors for online learning or those for traditional face-to-face learning but little is known about factors that predict blended learning effectiveness in view of learner characteristics and blended learning design features. This part of our study seeks to establish the learner characteristics/backgrounds and design features that predict blended learning effectiveness with regard to satisfaction, outcomes, motivation and knowledge construction. Song, Singleton, Hill, and Koh ( 2004 ) examined online learning effectiveness factors and found out that time management (a self-regulatory factor) was crucial for successful online learning. Eom, Wen, and Ashill ( 2006 ) using a survey found out that interaction, among other factors, was significant for learner satisfaction. Technical problems with regard to instructional design were a challenge to online learners thus not indicating effectiveness (Song et al., 2004 ), though the authors also indicated that descriptive statistics to a tune of 75% and time management (62%) impact on success of online learning. Arbaugh ( 2000 ) and Swan ( 2001 ) indicated that high levels of learner-instructor interaction are associated with high levels of user satisfaction and learning outcomes. A study by Naaj et al. ( 2012 ) indicated that technology and learner interactions, among other factors, influenced learner satisfaction in blended learning.

Objective and research questions of the current study

The objective of the current study is to investigate the effectiveness of blended learning in view of student satisfaction, knowledge construction, performance and intrinsic motivation and how they are related to student characteristics and blended learning design features in a blended learning environment.

Research questions

What are the student characteristics and blended learning design features for an effective blended learning environment?

Which factors (among the learner characteristics and blended learning design features) predict student satisfaction, learning outcomes, intrinsic motivation and knowledge construction?

Conceptual model of the present study

The reviewed literature clearly shows learner characteristics/background and blended learning design features play a part in blended learning effectiveness and some of them are significant predictors of effectiveness. The conceptual model for our study is depicted as follows (Fig.  1 ):

Conceptual model of the current study

Research design

This research applies a quantitative design where descriptive statistics are used for the student characteristics and design features data, t-tests for the age and gender variables to determine if they are significant in blended learning effectiveness and regression for predictors of blended learning effectiveness.

This study is based on an experiment in which learners participated during their study using face-to-face sessions and an on-line session of a blended learning design. A learning management system (Moodle) was used and learner characteristics/background and blended learning design features were measured in relation to learning effectiveness. It is therefore a planning evaluation research design as noted by Guskey ( 2000 ) since the outcomes are aimed at blended learning implementation at MMU. The plan under which the various variables were tested involved face-to-face study at the beginning of a 17 week semester which was followed by online teaching and learning in the second half of the semester. The last part of the semester was for another face-to-face to review work done during the online sessions and final semester examinations. A questionnaire with items on student characteristics, design features and learning outcomes was distributed among students from three schools and one directorate of postgraduate studies.

Participants

Cluster sampling was used to select a total of 238 learners to participate in this study. Out of the whole university population of students, three schools and one directorate were used. From these, one course unit was selected from each school and all the learners following the course unit were surveyed. In the school of Education ( n  = 70) and Business and Management Studies ( n  = 133), sophomore students were involved due to the fact that they have been introduced to ICT basics during their first year of study. Students of the third year were used from the department of technology in the School of Applied Sciences and Technology ( n  = 18) since most of the year two courses had a lot of practical aspects that could not be used for the online learning part. From the Postgraduate Directorate ( n  = 17), first and second year students were selected because learners attend a face-to-face session before they are given paper modules to study away from campus.

The study population comprised of 139 male students representing 58.4% and 99 females representing 41.6% with an average age of 24 years.

Instruments

The end of semester results were used to measure learner performance. The online self-regulated learning questionnaire (Barnard, Lan, To, Paton, & Lai, 2009 ) and the intrinsic motivation inventory (Deci & Ryan, 1982 ) were applied to measure the constructs on self regulation in the student characteristics and motivation in the learning outcome constructs. Other self-developed instruments were used for the other remaining variables of attitudes, computer competence, workload management, social and family support, satisfaction, knowledge construction, technology quality, interactions, learning management system tools and resources and face-to-face support.

Instrument reliability

Cronbach’s alpha was used to test reliability and the table below gives the results. All the scales and sub-scales had acceptable internal consistency reliabilities as shown in Table  1 below:

Data analysis

First, descriptive statistics was conducted. Shapiro-Wilk test was done to test normality of the data for it to qualify for parametric tests. The test results for normality of our data before the t- test resulted into significant levels (Male = .003, female = .000) thereby violating the normality assumption. We therefore used the skewness and curtosis results which were between −1.0 and +1.0 and assumed distribution to be sufficiently normal to qualify the data for a parametric test, (Pallant, 2010 ). An independent samples t -test was done to find out the differences in male and female performance to explain the gender characteristics in blended learning effectiveness. A one-way ANOVA between subjects was conducted to establish the differences in performance between age groups. Finally, multiple regression analysis was done between student variables and design elements with learning outcomes to determine the significant predictors for blended learning effectiveness.

Student characteristics, blended learning design features and learning outcomes ( RQ1 )

A t- test was carried out to establish the performance of male and female learners in the blended learning set up. This was aimed at finding out if male and female learners do perform equally well in blended learning given their different roles and responsibilities in society. It was found that male learners performed slightly better ( M  = 62.5) than their female counterparts ( M  = 61.1). An independent t -test revealed that the difference between the performances was not statistically significant ( t  = 1.569, df = 228, p  = 0.05, one tailed). The magnitude of the differences in the means is small with effect size ( d  = 0.18). A one way between subjects ANOVA was conducted on the performance of different age groups to establish the performance of learners of young and middle aged age groups (20–30, young & and 31–39, middle aged). This revealed a significant difference in performance (F(1,236 = 8.498, p < . 001).

Average percentages of the items making up the self regulated learning scale are used to report the findings about all the sub-scales in the learner characteristics/background scale. Results show that learner self-regulation was good enough at 72.3% in all the sub-scales of goal setting, environment structuring, task strategies, time management, help-seeking and self-evaluation among learners. The least in the scoring was task strategies at 67.7% and the highest was learner environment structuring at 76.3%. Learner attitude towards blended learning environment is at 76% in the sub-scales of learner autonomy, quality of instructional materials, course structure, course interface and interactions. The least scored here is attitude to course structure at 66% and their attitudes were high on learner autonomy and course interface both at 82%. Results on the learners’ computer competences are summarized in percentages in the table below (Table  2 ):

It can be seen that learners are skilled in word processing at 91%, email at 63.5%, spreadsheets at 68%, web browsers at 70.2% and html tools at 45.4%. They are therefore good enough in word processing and web browsing. Their computer confidence levels are reported at 75.3% and specifically feel very confident when it comes to working with a computer (85.7%). Levels of family and social support for learners during blended learning experiences are at 60.5 and 75% respectively. There is however a low score on learners being assisted by family members in situations of computer setbacks (33.2%) as 53.4% of the learners reported no assistance in this regard. A higher percentage (85.3%) is reported on learners getting support from family regarding provision of essentials for learning such as tuition. A big percentage of learners spend two hours on study while at home (35.3%) followed by one hour (28.2%) while only 9.7% spend more than three hours on study at home. Peers showed great care during the blended learning experience (81%) and their experiences were appreciated by the society (66%). Workload management by learners vis-à-vis studying is good at 60%. Learners reported that their workmates stand in for them at workplaces to enable them do their study in blended learning while 61% are encouraged by their bosses to go and improve their skills through further education and training. On the time spent on other activities not related to study, majority of the learners spend three hours (35%) while 19% spend 6 hours. Sixty percent of the learners have to answer to someone when they are not attending to other activities outside study compared to the 39.9% who do not and can therefore do study or those other activities.

The usability of the online system, tools and resources was below average as shown in the table below in percentages (Table  3 ):

However, learners became skilled at navigating around the learning management system (79%) and it was easy for them to locate course content, tools and resources needed such as course works, news, discussions and journal materials. They effectively used the communication tools (60%) and to work with peers by making posts (57%). They reported that online resources were well organized, user friendly and easy to access (71%) as well as well structured in a clear and understandable manner (72%). They therefore recommended the use of online resources for other course units in future (78%) because they were satisfied with them (64.3%). On the whole, the online resources were fine for the learners (67.2%) and useful as a learning resource (80%). The learners’ perceived usefulness/satisfaction with online system, tools, and resources was at 81% as the LMS tools helped them to communicate, work with peers and reflect on their learning (74%). They reported that using moodle helped them to learn new concepts, information and gaining skills (85.3%) as well as sharing what they knew or learned (76.4%). They enjoyed the course units (78%) and improved their skills with technology (89%).

Learner interactions were seen from three angles of cognitivism, collaborative learning and student-teacher interactions. Collaborative learning was average at 50% with low percentages in learners posting challenges to colleagues’ ideas online (34%) and posting ideas for colleagues to read online (37%). They however met oftentimes online (60%) and organized how they would work together in study during the face-to-face meetings (69%). The common form of communication medium frequently used by learners during the blended learning experience was by phone (34.5%) followed by whatsapp (21.8%), face book (21%), discussion board (11.8%) and email (10.9%). At the cognitive level, learners interacted with content at 72% by reading the posted content (81%), exchanging knowledge via the LMS (58.4%), participating in discussions on the forum (62%) and got course objectives and structure introduced during the face-to-face sessions (86%). Student-teacher interaction was reported at 71% through instructors individually working with them online (57.2%) and being well guided towards learning goals (81%). They did receive suggestions from instructors about resources to use in their learning (75.3%) and instructors provided learning input for them to come up with their own answers (71%).

The technology quality during the blended learning intervention was rated at 69% with availability of 72%, quality of the resources was at 68% with learners reporting that discussion boards gave right content necessary for study (71%) and the email exchanges containing relevant and much needed information (63.4%) as well as chats comprising of essential information to aid the learning (69%). Internet reliability was rated at 66% with a speed considered averagely good to facilitate online activities (63%). They however reported that there was intermittent breakdown during online study (67%) though they could complete their internet program during connection (63.4%). Learners eventually found it easy to download necessary materials for study in their blended learning experiences (71%).

Learner extent of use of the learning management system features was as shown in the table below in percentage (Table  4 ):

From the table, very rarely used features include the blog and wiki while very often used ones include the email, forum, chat and calendar.

The effectiveness of the LMS was rated at 79% by learners reporting that they found it useful (89%) and using it makes their learning activities much easier (75.2%). Moodle has helped learners to accomplish their learning tasks more quickly (74%) and that as a LMS, it is effective in teaching and learning (88%) with overall satisfaction levels at 68%. However, learners note challenges in the use of the LMS regarding its performance as having been problematic to them (57%) and only 8% of the learners reported navigation while 16% reported access as challenges.

Learner attitudes towards Face-to-face support were reported at 88% showing that the sessions were enjoyable experiences (89%) with high quality class discussions (86%) and therefore recommended that the sessions should continue in blended learning (89%). The frequency of the face-to-face sessions is shown in the table below as preferred by learners (Table  5 ).

Learners preferred face-to-face sessions after every month in the semester (33.6%) and at the beginning of the blended learning session only (27.7%).

Learners reported high intrinsic motivation levels with interest and enjoyment of tasks at 83.7%, perceived competence at 70.2%, effort/importance sub-scale at 80%, pressure/tension reported at 54%. The pressure percentage of 54% arises from learners feeling nervous (39.2%) and a lot of anxiety (53%) while 44% felt a lot of pressure during the blended learning experiences. Learners however reported the value/usefulness of blended learning at 91% with majority believing that studying online and face-to-face had value for them (93.3%) and were therefore willing to take part in blended learning (91.2%). They showed that it is beneficial for them (94%) and that it was an important way of studying (84.3%).

Learner satisfaction was reported at 81% especially with instructors (85%) high percentage reported on encouraging learner participation during the course of study 93%, course content (83%) with the highest being satisfaction with the good relationship between the objectives of the course units and the content (90%), technology (71%) with a high percentage on the fact that the platform was adequate for the online part of the learning (76%), interactions (75%) with participation in class at 79%, and face-to-face sessions (91%) with learner satisfaction high on face-to-face sessions being good enough for interaction and giving an overview of the courses when objectives were introduced at 92%.

Learners’ knowledge construction was reported at 78% with initiation and discovery scales scoring 84% with 88% specifically for discovering the learning points in the course units. The accomplishment scale in knowledge construction scored 71% and specifically the fact that learners were able to work together with group members to accomplish learning tasks throughout the study of the course units (79%). Learners developed reports from activities (67%), submitted solutions to discussion questions (68%) and did critique peer arguments (69%). Generally, learners performed well in blended learning in the final examination with an average pass of 62% and standard deviation of 7.5.

Significant predictors of blended learning effectiveness ( RQ 2)

A standard multiple regression analysis was done taking learner characteristics/background and design features as predictor variables and learning outcomes as criterion variables. The data was first tested to check if it met the linear regression test assumptions and results showed the correlations between the independent variables and each of the dependent variables (highest 0.62 and lowest 0.22) as not being too high, which indicated that multicollinearity was not a problem in our model. From the coefficients table, the VIF values ranged from 1.0 to 2.4, well below the cut off value of 10 and indicating no possibility of multicollinearity. The normal probability plot was seen to lie as a reasonably straight diagonal from bottom left to top right indicating normality of our data. Linearity was found suitable from the scatter plot of the standardized residuals and was rectangular in distribution. Outliers were no cause for concern in our data since we had only 1% of all cases falling outside 3.0 thus proving the data as a normally distributed sample. Our R -square values was at 0.525 meaning that the independent variables explained about 53% of the variance in overall satisfaction, motivation and knowledge construction of the learners. All the models explaining the three dependent variables of learner satisfaction, intrinsic motivation and knowledge construction were significant at the 0.000 probability level (Table  6 ).

From the table above, design features (technology quality and online tools and resources), and learner characteristics (attitudes to blended learning, self-regulation) were significant predictors of learner satisfaction in blended learning. This means that good technology with the features involved and the learner positive attitudes with capacity to do blended learning with self drive led to their satisfaction. The design features (technology quality, interactions) and learner characteristics (self regulation and social support), were found to be significant predictors of learner knowledge construction. This implies that learners’ capacity to go on their work by themselves supported by peers and high levels of interaction using the quality technology led them to construct their own ideas in blended learning. Design features (technology quality, online tools and resources as well as learner interactions) and learner characteristics (self regulation), significantly predicted the learners’ intrinsic motivation in blended learning suggesting that good technology, tools and high interaction levels with independence in learning led to learners being highly motivated. Finally, none of the independent variables considered under this study were predictors of learning outcomes (grade).

In this study we have investigated learning outcomes as dependent variables to establish if particular learner characteristics/backgrounds and design features are related to the outcomes for blended learning effectiveness and if they predict learning outcomes in blended learning. We took students from three schools out of five and one directorate of post-graduate studies at a Ugandan University. The study suggests that the characteristics and design features examined are good drivers towards an effective blended learning environment though a few of them predicted learning outcomes in blended learning.

Student characteristics/background, blended learning design features and learning outcomes

The learner characteristics, design features investigated are potentially important for an effective blended learning environment. Performance by gender shows a balance with no statistical differences between male and female. There are statistically significant differences ( p  < .005) in the performance between age groups with means of 62% for age group 20–30 and 67% for age group 31 –39. The indicators of self regulation exist as well as positive attitudes towards blended learning. Learners do well with word processing, e-mail, spreadsheets and web browsers but still lag below average in html tools. They show computer confidence at 75.3%; which gives prospects for an effective blended learning environment in regard to their computer competence and confidence. The levels of family and social support for learners stand at 61 and 75% respectively, indicating potential for blended learning to be effective. The learners’ balance between study and work is a drive factor towards blended learning effectiveness since their management of their workload vis a vis study time is at 60 and 61% of the learners are encouraged to go for study by their bosses. Learner satisfaction with the online system and its tools shows prospect for blended learning effectiveness but there are challenges in regard to locating course content and assignments, submitting their work and staying on a task during online study. Average collaborative, cognitive learning as well as learner-teacher interactions exist as important factors. Technology quality for effective blended learning is a potential for effectiveness though features like the blog and wiki are rarely used by learners. Face-to-face support is satisfactory and it should be conducted every month. There is high intrinsic motivation, satisfaction and knowledge construction as well as good performance in examinations ( M  = 62%, SD = 7.5); which indicates potentiality for blended learning effectiveness.

Significant predictors of blended learning effectiveness

Among the design features, technology quality, online tools and face-to-face support are predictors of learner satisfaction while learner characteristics of self regulation and attitudes to blended learning are predictors of satisfaction. Technology quality and interactions are the only design features predicting learner knowledge construction, while social support, among the learner backgrounds, is a predictor of knowledge construction. Self regulation as a learner characteristic is a predictor of knowledge construction. Self regulation is the only learner characteristic predicting intrinsic motivation in blended learning while technology quality, online tools and interactions are the design features predicting intrinsic motivation. However, all the independent variables are not significant predictors of learning performance in blended learning.

The high computer competences and confidence is an antecedent factor for blended learning effectiveness as noted by Hadad ( 2007 ) and this study finds learners confident and competent enough for the effectiveness of blended learning. A lack in computer skills causes failure in e-learning and blended learning as noted by Shraim and Khlaif ( 2010 ). From our study findings, this is no threat for blended learning our case as noted by our results. Contrary to Cohen et al. ( 2012 ) findings that learners’ family responsibilities and hours of employment can impede their process of learning, it is not the case here since they are drivers to the blended learning process. Time conflict, as compounded by family, employment status and management support (Packham et al., 2004 ) were noted as causes of learner failure and drop out of online courses. Our results show, on the contrary, that these factors are drivers for blended learning effectiveness because learners have a good balance between work and study and are supported by bosses to study. In agreement with Selim ( 2007 ), learner positive attitudes towards e-and blended learning environments are success factors. In line with Coldwell et al. ( 2008 ), no statistically significant differences exist between age groups. We however note that Coldwel, et al dealt with young, middle-aged and old above 45 years whereas we dealt with young and middle aged only.

Learner interactions at all levels are good enough and contrary to Astleitner, ( 2000 ) that their absence makes learners withdraw, they are a drive factor here. In line with Loukis (2007) the LMS quality, reliability and ease of use lead to learning efficiency as technology quality, online tools are predictors of learner satisfaction and intrinsic motivation. Face-to-face sessions should continue on a monthly basis as noted here and is in agreement with Marriot et al. ( 2004 ) who noted learner preference for it for facilitating social interaction and communication skills. High learner intrinsic motivation leads to persistence in online courses as noted by Menager-Beeley, ( 2004 ) and is high enough in our study. This implies a possibility of an effectiveness blended learning environment. The causes of learner dissatisfaction noted by Islam ( 2014 ) such as incompetence in the use of the LMS are contrary to our results in our study, while the one noted by Hara and Kling, ( 2001 ) as resulting from technical difficulties and ambiguous course instruction are no threat from our findings. Student-teacher interaction showed a relation with satisfaction according to Swan ( 2001 ) but is not a predictor in our study. Initiating knowledge construction by learners for blended learning effectiveness is exhibited in our findings and agrees with Rahman, Yasin and Jusof ( 2011 ). Our study has not agreed with Eom et al. ( 2006 ) who found learner interactions as predictors of learner satisfaction but agrees with Naaj et al. ( 2012 ) regarding technology as a predictor of learner satisfaction.

Conclusion and recommendations

An effective blended learning environment is necessary in undertaking innovative pedagogical approaches through the use of technology in teaching and learning. An examination of learner characteristics/background, design features and learning outcomes as factors for effectiveness can help to inform the design of effective learning environments that involve face-to-face sessions and online aspects. Most of the student characteristics and blended learning design features dealt with in this study are important factors for blended learning effectiveness. None of the independent variables were identified as significant predictors of student performance. These gaps are open for further investigation in order to understand if they can be significant predictors of blended learning effectiveness in a similar or different learning setting.

In planning to design and implement blended learning, we are mindful of the implications raised by this study which is a planning evaluation research for the design and eventual implementation of blended learning. Universities should be mindful of the interplay between the learner characteristics, design features and learning outcomes which are indicators of blended learning effectiveness. From this research, learners manifest high potential to take on blended learning more especially in regard to learner self-regulation exhibited. Blended learning is meant to increase learners’ levels of knowledge construction in order to create analytical skills in them. Learner ability to assess and critically evaluate knowledge sources is hereby established in our findings. This can go a long way in producing skilled learners who can be innovative graduates enough to satisfy employment demands through creativity and innovativeness. Technology being less of a shock to students gives potential for blended learning design. Universities and other institutions of learning should continue to emphasize blended learning approaches through installation of learning management systems along with strong internet to enable effective learning through technology especially in the developing world.

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Kintu, M.J., Zhu, C. & Kagambe, E. Blended learning effectiveness: the relationship between student characteristics, design features and outcomes. Int J Educ Technol High Educ 14 , 7 (2017). https://doi.org/10.1186/s41239-017-0043-4

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Exploring Definitions, Models, Frameworks, and Theory for Blended Learning Research

2021, Blended learning: Research perspectives, Volume 3

This chapter begins by establishing a common ground for understanding how blended learning (BL) is defined in terms of the principal dimensions of media, method, and modality. The challenge of using BL as a treatment effect in research is explored, along with an argument for specifying blended models in terms of pedagogical as well as physical dimensions of the blend. The chapter includes a taxonomy for theoretical frameworks in BL research along with guidance for identifying a good theoretical contribution. Finally, this research relates examples of both imported and originary theoretical contributions to BL research in issues concerning instructional design, institutions, students, and faculty and provides implications for future research.

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Research in Blended Learning Final Report

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Anoush Margaryan

Copenhagen Business School - Department of Digitalisation

Date Written: May 31, 2024

As a cross-institutional, cross-disciplinary, strategic initiative at Copenhagen Business School (CBS),  Research in Blended Learning (RiBL) pursued a dual - research and development - goal of  advancing the understanding of fundamental processes and practices underpinning  technology-enhanced teaching and learning and  developing and testing practical applications, toolkits, and recommendations to inform and  guide policy and practice of technology-enhanced learning and teaching at CBS and within  higher business education and lifelong learning more broadly.  Supported by a generous endowment from Candys Foundation Denmark and match-funding  provided by CBS, RiBL brought together over 30 faculty, researchers, and administrative staff f rom across CBS departments and central units to take forward its research and development  programme in 2017-2024.  RiBL’s activities were structured into four broad themes:  Theme 1. Effects and outcome measures of blended learning  Theme 2. Changing roles of teachers and students  Theme 3. The future of higher education  Theme 4. Bridging higher education and emergent work practices. 

Keywords: blended learning, technology-enhanced learning, higher education

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• Published: 08 July 2023

Adoption of blended learning: Chinese university students’ perspectives

• Teng Yu 1 , 2 ,
• Jian Dai 3 , 4 &
• Chengliang Wang 4 , 5  

Humanities and Social Sciences Communications volume  10 , Article number:  390 ( 2023 ) Cite this article

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Against the backdrop of the deep integration of the Internet with learning, blended learning offers the advantages of combining online and face-to-face learning to enrich the learning experience and improve knowledge management. Therefore, the objective of this present study is twofold: a. to fill a gap in the literature regarding the adoption of blended learning in the post-pandemic era and the roles of both the technology acceptance model (TAM) and the theory of planned behavior (TPB) in this context and b. to investigate the factors influencing behavioral intention to adopt blended learning. For that purpose, the research formulates six hypotheses, incorporates them into the proposed conceptual model, and validates them using model-fit indices. Based on data collected from Chinese university students, the predicted model’s reliability and validity are evaluated using structural equation modeling (SEM). The results of SEM show that (a) the integrated model based on the TAM and the TPB can explain 67.6% of the variance in Chinese university students’ adoption of blended learning; (b) perceived usefulness (PU), perceived ease of use (PEU), and subjective norms (SN) all have positive impacts on learning attitudes (LA); (c) PEU has a positive influence on PU, and SN has a positive influence on perceived behavioral control (PBC); and (d) both PU and LA have a positive influence on the intention to adopt blended learning (IABL). However, PEU, SN, and PBC have little effect on IABL; e. LA mediates the effect of PU on IABL, and PU mediates the effect of PEU on IABL. This study demonstrated that an integrated conceptual framework based on the TAM and the TPB as well as the characteristics of blended learning offers an effective way to understand Chinese university students’ adoption of blended learning.

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Introduction.

Due to the spread of COVID-19, certain conventional face-to-face teaching methods became inappropriate for the current teaching situation. By July 2020, more than 180 countries had closed their schools due to the outbreak. Worldwide, online learning offerings were also reevaluated to meet the difficulties of the global educational environment (UNESCO, 2020 ). As a consequence of the hazards that COVID-19 posed to teaching and learning, students were compelled to shift from face-to-face to online learning (Yu et al., 2021 ), with the majority of courses in China opting for blended learning (Kang et al., 2021 ). In February 2020, China became the first country to announce the launch of online courses. The question of whether online learning could replace traditional offline education has sparked heated debate in China (Jin et al., 2021 ). Moreover, the Ministry of Education of China issued an announcement in 2021 claiming that it was essential to accelerate the development of new infrastructure, such as intelligent teaching spaces or campuses, as well as to promote blended learning (Yang et al., 2022 ). Online learning was adopted in China, reducing the consequences of school shutdowns across the country and slowing the virus’s spread. Nevertheless, the government was required to address issues concerning what to educate, how to teach, and how to provide fundamental necessities such as education infrastructure. The Chinese Ministry of Education offered a variety of teaching platforms that allowed students to take online lessons via their laptops, desktop computers, cell phones, etc. (Zhang et al., 2020 ).

Driven by the rapidly changing digital ecosystem, the design and application of blended teaching modes have become important components of the reform of teaching methods in colleges and universities. Blended learning refers to the organic integration of online and offline learning, which may not only guide and inspire students’ learning but also arouse students’ enthusiasm and autonomy with respect to learning. In addition, Pulham and Graham ( 2018 ) identified the top 20 blended teaching skills. Many elements impact students’ reactions when these technologies are utilized in the learning process (Xu et al., 2021 ). Because the actors involved in educational process change, these frameworks undergo constant alteration. Rapid changes in users’ digital abilities and attitudes toward technology can be observed (Lazar et al., 2020 ).

Since that time, the COVID-19 pandemic has exhibited the ordinary trend of ups and downs. Hence, the pandemic has continued for a long period and thus had a substantial influence on many parts of society, particularly education (Cahapay, 2020 ). The extensive application of online teaching has led to many problems, such as low satisfaction with the teaching effect, low willingness to continue using this type of teaching, and instructional techniques that must be enhanced. Consequently, when users’ expectations are met, their contentment increases (Cheng et al., 2019 ; Kim, 2010 ); conversely, if their perceived performance falls short of what they had expected, their satisfaction decreases (Mellikeche et al., 2020 ). However, Popa et al. ( 2020 ) proposed that offline education provides the benefits of real-world experience, ease of engaging in diverse activities, cultural value exchange, and simple management and service, which are absent in online education. As a result, the present model of education is gradually evolving, and the mix of online and offline instruction is leading to a major educational revolution.

The inclusion of technology in face-to-face education has aroused a great deal of interest and has opened up several areas of study over the years. Because of its perceived efficiency in offering flexible, timely, and continual learning, blended learning is currently widely regarded as the most popular and effective instruction mode used in educational institutions. Students must adjust to new blended learning techniques and environments with the assistance of modern technologies (Mo et al., 2022 ). In Chinese universities, faculty should be encouraged to develop courses based on the features of their local institutions during the post-pandemic phase to decrease the learning costs associated with blended learning and the time required to install e-learning platforms. As a result, the teaching effect of blended learning can be enhanced (Lin et al., 2021 ).

Blended learning has received significant attention and has been widely recognized as “the new normal” based on several influential studies (Dziuban et al., 2018 ). These studies have highlighted the numerous advantages associated with blended learning. Students, for example, are expected to manage and complete their studies independently of their teacher and at their own pace, so they must have self-regulation abilities and technological proficiency when utilizing online technology outside of their offline meetings. Moreover, to properly utilize and operate technology in teaching as well as develop and post learning materials to students, instructors must be technologically savvy (e.g., with regard to creating quality online videos). Furthermore, educational institutions must offer both instructors and students essential training and technical help to facilitate the successful use of existing technologies as well as the efficient use of the online component (Rasheed et al., 2020 ).

As a result, importance has been attached to the task of promoting acceptance of blended learning, which is a significant concern for Chinese university students. Several studies have demonstrated the challenges faced by students, teachers, and educational institutions (Ocak, 2011 ; Broadbent, 2017 ; Medina, 2018 ; Prasad et al., 2018 ). Nevertheless, these studies have been restricted in terms of their capacity to provide a solution to this problem. Furthermore, while several previous studies have focused on the formulation and application of the blended teaching model based on the teaching cases associated with the course, empirical research on the adoption of blended learning from the standpoint of students and the Chinese scenario has been distinctly lacking. Mustafa and Garcia ( 2021 ) found that various information system (IS) theories have been integrated into the technology acceptance model (TAM) to improve our understanding of the intention to adopt online learning. Their results showed that task-technology fit (TTF) theory and the theory of planned behavior (TPB) are popular and successful theories that have been combined with the TAM. The TAM is a widely used theoretical framework in information systems and technology research, which aims to understand and predict how users adopt and accept new technologies (Davis, 1985 ). On the other hand, the TPB is a social psychological theory predicting human behavior (Ajzen, 1985 , 1991 ; Ajzen and Fishbein, 2000 ). In addition, the research on which they focused is unusual in that it employed an integrated conceptual adoption framework based on the TAM and the TPB from the perspective of Chinese undergraduates for the first time. This approach was the first systematic attempt to scientifically explore and evaluate the variables of students’ acceptance and usage of blended learning (Virani et al., 2020 ).

In the post-pandemic era, blended learning is still an efficient and flexible mode that Chinese university students can adopt because it enables them to interact more closely, engage in more abundant experiences, and improve their understanding (Blain et al., 2022 ; Müller and Wulf, 2022 ; Wu and Luo, 2022 ; Yang and Ogata, 2022 ). Blended learning can not only assist students in acquiring the habit of self-study but also shed light on creative ways to overcome problems. After interviewing 48 participants in a semi-structured manner, Fletcher et al. ( 2022 ) discovered that blended learning is an ideal tool for learners in a post-COVID future. However, Gómez et al. ( 2022 ) claimed that face-to-face interactions should be incorporated into blended learning during the post-COVID-19 era. Furthermore, Callaghan et al. ( 2022 ) noted that learners perceived their technology exposure as establishing an atmosphere that can have a long-term influence, which is a cognitive tool for learning. Blended learning is a learner-centered and integrated way for learners to study in both the pre-COVID to post-COVID eras that can be investigated through exploratory survey research. Hence, blended teaching is just as popular in the post-pandemic period as it was during the pandemic. To fill the research gap regarding blended learning in a post-pandemic era, this study expands the integrated theory of the TAM and the TPB.

Based on previous debates and in response to the demand for additional data-driven research on the motivation for blended learning (Deng et al., 2019 ), the current study aims to address the following two questions:

RQ1. To what extent can an integrated conceptual adoption framework based on the TAM and the TPB explain Chinese university students’ adoption of blended learning?

RQ2. What factors influence university students’ adoption of blended learning?

This research is intended to contribute to arguments about the adoption of blended learning. This paper intends to construct a conceptual model that explains university students’ intention to adopt blended learning (IABL) by integrating the TAM and the TPB and to validate five explanatory variables in this context, including perceived usefulness (PU), perceived ease of use (PEU), learning attitudes (LA), subjective norms (SN) and perceived behavioral control (PBC).

Literature review and research hypotheses

Blended learning.

Defined as “a judicious blending of face-to-face learning experiences in the classroom with online activities”, blended learning combines face-to-face teaching with technology-mediated teaching (Garrison and Kanuka, 2004 ; Porter et al., 2014 ). Since the beginning of the twenty-first century, educational institutions have adopted a variety of approaches that blend online instruction with traditional face-to-face instruction, an approach which is known as blended, flipped, mixed, or inverted learning.

Combining educational resources with online interaction has improved the traditional face-to-face model and the entire online form of instruction. Namely, when implemented correctly, this strategy combines the advantages of both the face-to-face and online learning modes of training (Broadbent, 2017 ; Darling-Aduana and Heinrich, 2018 ). Blended learning, for example, decreases barriers between professors and their students in online transactions and enhances interaction (Jusoff and Khodabandelou, 2009 ). Not only does it provide adaptability, educational depth, and cost-effectiveness (Graham, 2006 ), it also promotes value interaction and learning participation (Dziuban et al., 2004 ). Hence, it is beneficial for various types of students (Heinze and Procter, 2004 ).

Due to the popularity of the “Internet+” education application, the notion of blended teaching has steadily emerged (Bai et al., 2016 ; Tang et al., 2020 ; Míguez-Álvarez et al., 2020 ). Hence, blended learning has been regarded as the third generation of advancement in higher education. Traditional face-to-face education is the first generation, and e-learning education is the second generation (Park et al., 2019 ; Dang et al., 2016 ), although it is becoming more common at all educational levels. Blended learning tools can bridge the gap between traditional offline and online learning based on networks. A significant portion of the blended learning curriculum (between 30% and 80%) is delivered online (Bazelais et al., 2018 ). According to this rationale, the blended educational approach may be regarded as a contemporary technique used to facilitate teaching and learning. Some institutes use flipped classroom arrangements, which combine offline and online instruction (Kasat et al., 2019 ). This approach is a novel learning paradigm that supplements traditional course teaching with online activities (Benbunan-Fich, 2008 ). Therefore, for developing countries such as China, blended learning is an acceptable technology because of the issues they face, such as a large number of students, scarce resources, tight budgets, and limited space (Halan, 2005 ; Virani et al., 2020 ). Blended learning involves a restructuring of curriculum design that aims to activate students’ initiative in participating in online learning (Yin and Yuan, 2021 ).

According to several studies, incorporating information technology (IT) into the teaching process enhances course access and learning opportunities (Turvey and Pachler, 2020 ). Compared to traditional teaching, arousing students’ interest, fostering their enthusiasm, and inspiring their imagination and self-learning consciousness are all positive effects of blended education (Popa et al., 2020 ).

Technology acceptance model

The TAM, which was initially proposed by Davis ( 1985 ) based on the Theory of Reasoned Action (TRA) developed by Fishbein and Ajzen ( 1975 ), is a significant model used to research the variables that influence consumers’ acceptance of information system technology. Developed by Davis et al. ( 1989 ), the TAM is useful for describing and predicting user behavioral intentions regarding information systems. Venkatesh et al. ( 2003 ) claimed that behavioral intention, which has bene widely recognized as an agent of acceptance, is the most direct antecedent of technology use, according to the TAM, and is also a fully validated predictor of actual behavior (Tao et al., 2018 ). PU and PEU are two beliefs that influence behavioral intention. The extent to which an individual perceives that employing technology can boost their ability to accomplish their tasks is known as PU, and the degree to which they feel that doing so will be labor-free is known as PEU (Davis et al., 1989 ). PEU also has a significant and beneficial influence on PU. Two important primary views within the TAM, i.e., PU and PEU, were developed by synthesizing self-efficacy theory, expectation theory, etc. In addition, the TAM, which includes behavior intention, attitudes, actual usage, and external variables, can explain or predict factors that impact the use of IT (Straub et al., 1995 ).

The TAM has proven to be capable of explaining technological acceptance in various situations, including information systems for online banking (Chandio et al., 2017 ), informatics in health (Tao et al., 2018 ), apps for social networks (Chen et al., 2019 ), internet banking services (Patel and Patel, 2018 ), autonomous vehicles (Zhang et al., 2019 ), digital technology in education (Scherer et al., 2019 ), mobile tourism apps (Chen and Tsai, 2019 ), and self-driving cars (Jászberényi et al., 2022 ), etc. In MOOCs and other e-learning applications, these models have also been explored and expanded (Agudo-Peregrina et al., 2014 ; Balaman and Baş, 2021 ; Fianu et al., 2018 ; Hsu et al., 2018 ; Scherer et al., 2019 ; Šumak et al., 2011 ; Wang et al., 2020 ; Wu and Chen, 2017 ; Yoon, 2016 ). Furthermore, the TAM has been used in various studies to assess learners’ intentions to continue using e-learning systems. Chow et al. ( 2012 ) showed that when utilizing well-known online study platforms, PU and PEU can boost learning motivation. Hence, they have positive effects on learning through the platform. Similarly, Abdullah and Ward ( 2016 ), Islam ( 2013 ), Ali et al. ( 2013 ), and Zhou et al. ( 2021 ) employed the TAM to investigate the impacts of the online system of learning, indicating that PU and PEU can influence the results. Recently, Bai and Jiang ( 2022 ) found that the TAM offers influencing factors that support the use of digital resources according to a meta-analysis of 19 research articles. The TAM is thus a sound and reliable paradigm according to the data. Additionally, Alqahtani et al. ( 2022 ) utilized the TAM to investigate students’ perceptions of continuing to use online platforms following the outbreak of COVID-19. As a result, the TAM was used as a foundational theory in this study to examine the behavioral intentions associated with blended learning. However, this model focuses on the effect of perceptual traits rather than the influence of social aspects, and its explanatory capacity might be improved. Therefore, empirical research on the intention of university students to adopt blended learning using the TAM remains limited (Al-Azawei et al., 2017 ). Simultaneously, given the limits of the TAM in terms of interpretation, this study seeks to combine the TAM with the TPB to uncover the influencing mechanism underlying university students’ intention to adopt blended learning more effectively.

Theory of planned behavior

According to the theory of planned behavior (TPB) (Ajzen, 1985 , 1991 ; Ajzen and Fishbein, 2000 ), attitudes influence a particular behavior indirectly because of the relationship between SN and PBC. Given a strong desire to perform a specific task, that task is more likely to be completed. A person’s attitudes toward the activity show how much that person appreciates given conduct and whether the person anticipates that behavior will result in the associated consequences and values. The evaluation of one’s resources, abilities, and competencies with regard to the relevant action is referred to as PBC. Although behavioral intention mediates the effects of attitudes and SN on a particular behavior (Fishbein, 1979 ), Ajzen ( 1985 ) contended that the influence of PBC on behavior becomes manifest both directly and indirectly via behavioral intention (Knauder and Koschmieder, 2019 ).

On a metatheoretical level, based on the TRA of Fishbein and Ajzen ( 1975 ; 1980 ), Ajzen ( 1985 ) proposed in the TPB that individuals systematically analyze information and behave in terms of their outcomes (benefits). These outcomes have been perceived subjectively as the expectations of others who are significant to the individual in question. The TPB has been objectively validated by several academics across various investigations in contexts such as travel destination (Yuzhanin and Fisher, 2016 ), academic dishonesty (Hendy and Montargot, 2019 ), sports participation (St Quinton, 2022 ), private renting (Li et al., 2022 ), and waste sorting (Bardus and Massoud, 2022 ). Zaremohzzabieh et al. ( 2019 ) and Ashaduzzaman et al. ( 2022 ) conducted a meta-analysis to examine the applicability of the TPB. Several academics have empirically validated the TPB in studies on online learning (Hadadgar, et al., 2016 ; Chu and Chen, 2016 ; Sungur-Gül and Ateş, 2021 ).

Theories of integrated TAM and TPB

This study employs the TAM (Davis et al., 1989 ) and the TPB (Ajzen, 1991 ) to evaluate the factors influencing university students’ intentions to accept blended learning. The TAM has typically been used by academics to uncover elements impacting customers to adopt the new technology. Based on the TAM, individuals’ PU and PEU with respect to the technology influence the attitudes, intentions, and behaviors of customers (Davis et al., 1989 ). In addition, organizational issues impact the PU and PEU associated with technology (Yousafzai et al., 2007 ). Blended learning research has shown that certain factors have significant effects on learning success, i.e., PU, LA, SN, PBC, and learning behavior; however, PEU does not have such an impact (Wang et al., 2020 ).

Nevertheless, one drawback of the TAM is that it ignores the function of SN, which is similarly crucial in assessing the intentions of individuals (Venkatesh and Davis, 2000 ). Personal expectations from society regarding whether to perform a given action are referred to as SN (Hsiao and Tang, 2014 ). SN is thus one of the most essential aspects of the TPB. According to the TPB, attitudes, SN, and PBC may explain human behavior (Ajzen, 1991 ). In terms of PU and PEU, the TAM, on the other hand, highlights the attitudes variable in the TPB (Sun et al., 2013 ). Furthermore, the SN has a comparable social influence (Thompson et al., 1991 ). Therefore, acceptance tends to be a mix of technical excellence and user characteristics. With regard to indirect factors, social influences (SI) and facilitating conditions (FC) within the organization also have an impact on acceptance (Menant et al., 2021 ). Several academics have empirically supported the integration of the TAM with the TPB in various contexts, such as the use of social media for transactions (Hansen et al., 2018 ), physical activity (Tweneboah-Koduah et al., 2019 ), drone food delivery services (Choe et al., 2021 ), telecommuting during the COVID-19 outbreak (Chai et al., 2022 ), and electric vehicle purchases (Vafaei-Zadeh et al., 2022 ).

Accordingly, this study integrated the TAM with the TPB. Hence, a new research model for blended learning can be developed to illustrate how PU, PEU, LA, SN, and PBC are crucial elements that can impact learners’ intention to adopt blended learning. Figure 1 illustrates the study’s framework.

H1–H11 refer to the 11 hypotheses.

Research hypotheses

Perceived usefulness.

Defined as the extent to which students feel that blended learning may increase their study efficiency (Davis et al., 1989 ), PU is regarded as a predictor of attitudes toward behavioral intention. According to a basic model, PU has a favorable effect on behavioral intention. In addition, it frequently influences willingness as mediated by attitudes (Bazelais et al., 2018 ; Chen and Lu, 2016 ; Zhou et al., 2021 ). The role of PU in behavioral intention has previously been investigated extensively, and the results have shown that PU is a substantial predictor of behavioral intention (Chai et al., 2022 ; Gao et al., 2019 ; Jin et al., 2021 ; Kamal et al., 2020 ; Menant et al., 2021 ; Patel and Patel, 2018 ; Scherer et al., 2019 ; Sharma, 2019 ). PU is related to students’ perceptions of blended teaching’s efficacy in increasing their learning results. Teo and Dai ( 2022 ) claimed that blended learning can flexibly organize learning time, foster active learning awareness, and improve teacher-student interaction. Students adopt blended learning if its impact is significant. Instead, refusing the integrated education approach can have a negative effect. Thus, the following hypotheses are proposed:

H1: PU positively influences LA toward blended learning.

H2: PU positively influences IABL.

Perceived ease of use

The extent to which students perceive the task of engaging in blended learning to be physically and cognitively challenging is referred to as PEU. The PEU of an item indicates how well it may be comprehended or used. People prefer to utilize considerably easier items (Davis et al., 1989 ; Lazar et al., 2020 ; Sharma, 2019 ). As noted by Venkatesh et al. ( 2003 ), students are highly concerned about the complexity of the combination of online learning and offline discussion involved in the blended learning process. According to Wu and Liu ( 2013 ), one primary condition for evaluating perceived utility is PEU. Hence, the more students experience the PEU of blended learning courses, the more eager they are to participate in blended learning, and the simpler it is for them to experience the impacts of blended teaching. As a result, the following hypotheses are proposed in this study:

H3: PEU positively impacts LA toward blended learning.

H4: PEU positively impacts PU of blended learning.

H5: PEU positively impacts IABL.

Learning Attitudes

Attitudes, as a psychological process, determine whether a person likes or dislikes something (Sreen et al., 2018 ; Balaman and Baş, 2021 ). Described as “the extent of a person’s positive or negative judgment or evaluation of the action in issue” (Fishbein and Ajzen, 1975 ), a previous study highlighted the substantial empirical relationship between attitudes and willingness to continue (Ajzen, 1991 ). Subjective appraisal of participation in blended learning for students is reflected in their behavioral attitudes. Students’ readiness to accept blended learning increases if they have positive attitudes toward participation in blended teaching. In contrast, if students’ cognitive and emotional attitudes are insufficiently favorable to participate in blended learning, this situation can diminish students’ desire to accept blended teaching (Tao et al., 2022 ). The better the college student’s attitudes toward engaging in and using the blended learning model, the more that student is to accept it (Venkatesh et al., 2003 ; Wu and Liu, 2013 ). Therefore, this study proposes the following hypothesis:

H6: LA has a positive impact on IABL.

Subjective norms

The perceived social expectation regarding a certain behavior is denoted by SN (Bardus and Massoud, 2022 ; Collins et al., 2011 ; Chu and Chen, 2016 ). This notion applies to the concept that particular individuals or groups support and promote specific behaviors. (Han et al., 2020 ; Knauder and Koschmieder, 2019 ; Shalender and Sharma, 2021 ; Sungur-Gül and Ateş, 2021 ). In other words, if a large number of people who are significant to individuals do something that is beneficial for the environment, the individual in question also tend to be sufficiently sensitive or sensible to emulate this behavior (Ashaduzzaman et al., 2022 ; Cialdini et al., 1990 ; Hendy and Montargot, 2019 ). The SN associated with embracing blended learning refers to the expectations of and compliance demands made by classmates, professors, and other relevant groups when college students participate in blended learning. Therefore, university students are impacted by their peers, class norms, and professors’ expectations. They are pushed by a variety of public viewpoints and may even feel alienated if they do not follow the standards thus expressed. SN significantly impacts the desire to utilize current instructional technologies in colleges and universities according to an empirical study by Venkatesh et al. ( 2003 ) and Asare et al. ( 2016 ). Nevertheless, previous research on whether SN influences students’ readiness to adopt blended learning has not been thorough (Dakduk et al., 2018 ; Hadadgar et al., 2016 ; Prasad et al., 2018 ). Hence, the following hypotheses are proposed:

H7: SN positively impacts LA.

H8: SN positively impacts PBC.

H9: SN positively impacts IABL.

Perceived behavioral control

According to Ajzen ( 1991 ), PBC is one of the primary elements that impact the adoption of courses in blended learning. PBC relates to the degree to which students believe that they have control over their time, energy, and resources when participating in blended learning. In addition, PBC is beneficial for learners to improve their overall performance and academic accomplishment. In terms of PBC (or self-efficacy beliefs), MacFarlane and Woolfson ( 2013 ) reported specific relationships among a general sense of optimistic self-efficacy, reform implementation, and the ability to meet challenges. The intensity of PBC is primarily influenced by external facilitation circumstances and self-efficacy. First, external promotion factors mainly indicate the controllability of external conditions when students participate in blended learning, such as time and network equipment status. Oh and Yoon ( 2014 ) and Asare et al. ( 2016 ) claimed that a lack of external circumstances decreases students’ desire to take online courses. Second, self-efficacy is related to the students’ belief that they are qualified for blended learning. Students must examine the difficulty of a particular learning assignment, assess their ability’s fit with the learning task in question, and assess their competence to complete the task. Therefore, this study proposes the following hypothesis:

H10: PBC positively impacts IABL.

The mediating roles of learning attitudes and perceived usefulness

Based on the preceding discussion and analysis of the relationships among PU, PEU, LA, and IABL, the higher the degree of PU of blended learning experienced by Chinese university students, the higher their LA toward participation, which positively influences IABL. Likewise, the higher the degree of PEU of blended learning experienced by Chinese university students, the higher their PU toward participation, which positively influences IABL. Moreover, active engagement in blended learning initiatives has the potential to stimulate university students’ willingness to embrace this pedagogical method (Venkatesh et al., 2003 ; Wu and Liu, 2013 ).

LA is a mediating variable that positively influences Chinese university students’ intention to adopt blended learning. Mustafa et al. ( 2021 ) found that PU can significantly influence users’ intention to take advantage of a particular format of library resources through their positive attitudes. Jaiswal et al. ( 2021 ) concluded that changing the user’s attitudes can increase the user’s likelihood of exhibiting the intended adoption behavior and verified that people’s attitudes toward EVs mediate the positive effect of PU on their adoption intention.

In summary, the following hypotheses are proposed:

H11: LA mediates the effect of PU on IABL.

H12: PU mediates the effect of PEU on IABL.

Based on these hypotheses, this study develops a conceptual framework by integrating the TAM and the TPB to explain university students’ willingness to accept blended learning. Taking IABL as the explained variable, PU, PEU, IABL, SN, and PBC are regarded as explanatory variables (Fig. 1 ).

Research methodology

The study model underwent validation using structural equation modeling (SEM), which is a statistical method used to generate, estimate, and evaluate causal relationships. Unlike standard regression analysis, SEM can handle multiple dependent variables simultaneously as well as independent latent variables, thus facilitating the comprehensive examination and assessment of various theoretical models. Strong inferences from structural model testing, as shown in SEM treatments (Barrett, 2007 ; Kline, 2015 ), are contingent on a high sample size (i.e., at least 200 cases). Regarding the number of questionnaire samples, Loehlin ( 2004 ) discovered that the median of the paper data samples was 198 after counting 72 SEM papers. Barrett ( 2007 ) believed that the number of samples should be eight times greater than the number of model variables. However, he also noted that the built-in maximum likelihood method is generally utilized when SEM is implemented. The chi-square value becomes dramatically inflated as the sample size surpasses 500, resulting in poor model fit. As a result, scholars have generally recommended that the sample size be between 200 and 500. In addition, SEM represents a statistical technique that has been extensively employed to investigate the intricate interrelationships among multiple variables (Eksail and Afari, 2020 ). Within the scope of this study, meticulous scrutiny is directed toward the associations that exist between the observed variables and their underlying constructs, in line with the seminal work of Kline ( 2023 ). The present inquiry effectively harnesses the capabilities of SEM, as it allows for the simultaneous examination of variables while facilitating the independent estimation of the errors associated with each variable, as noted by Kline ( 2023 ). Moreover, this method facilitates the concurrent utilization of multiple indicator variables per construct, contributing to the generation of more robust inferences at the construct level when contrasted with conventional regression methodologies (Teo, 2009 ). Hence, SEM was deemed appropriate for this investigation. The research instrument and SEM are tested and reported separately in the next section.

Participants and procedure

University students drawn from various campuses across mainland China who were either enrolled at the time or had previously completed at least one blended learning course participated in the survey. They were invited to complete the surveys. In addition, the members of the project took the initiative to contact teachers responsible for blended teaching at various universities to learn more about the students’ taking courses in blended learning. These teachers were asked to distribute paper or online surveys in the classroom. The university students completed the questionnaires voluntarily and were not compensated for their participation. From February through April 2022, a total of 233 questionnaires were collected. After excluding 32 incomplete and unclear surveys, 201 valid responses remained, resulting in an effective response rate of 86%. The outliers were removed because they might have led to inaccurate statistical results, according to Hair et al. ( 2012 ). The researchers utilized convenience sampling to select participants in this study, as this method offers certain advantages such as geographical proximity, easy accessibility, availability within a specific timeframe, and voluntary participation (Etikan et al., 2016 ). Based on the descriptive statistics of the sample, male students and female students accounted for 40.8% and 59.2% of the total population, respectively. Both undergraduate and postgraduate students at universities were included in the sample to provide a comprehensive representation of the student population. A total of 77.6% of the respondents were undergraduates, while 22.4% were postgraduates. The respondents’ general information is as follows (Table 1 ).

Research instrument

The researchers created the questionnaire based on previous comparable studies (Davis et al., 1989 ; Venkatesh et al., 2003 ; Wu and Liu., 2013 ; Ajzen and Fishbein, 2000 ) since no specialized questionnaire was available in the literature to directly collect feedback from Chinese university students. These inquiries were taken from these comparable studies and adjusted as necessary for this investigation. Because the outcomes of this discovering structure were not covered by the adopted instrument, only a few inquiries were added to the investigative tool. These initiatives were further modified to suit the current study context after being validated by previous studies on the technology acceptance of blended learning (see Table 2 ). Moreover, the items were back-translated by two scholars who were fluent in Chinese and English (Brislin, 1970 ). The instrument is divided into two sections: the first section records the demographic data of the respondents (see Table 1 ), while the second section contains questions intended to gauge the constructs included in the suggested theoretical model (see Table 2 ). Six latent variables are included in the scale design: PEU, PU, LA, SN, PBC, and IABL. Responses are scored on a seven-point Likert scale (ranging from 7: totally agree to 1: totally disagree) for the observed variables of each concept. The initial questionnaire consisted of four demographic questions and 24 items pertaining to the research model. First, a pilot test was conducted, and 46 test data points were collected for analysis. Two items that did not meet the relevant standards in terms of the modification indices were deleted based on the results of the analysis. Therefore, 22 items remained and were included in the formal questionnaire (see Table 2 ). The scales used were all derived from authoritative and mature questionnaires, which can to a certain extent guarantee the face and content validity of the questionnaires used. Simultaneously, this questionnaire was examined and debated by three professors working in the field of blended learning and received their unanimous affirmation, thus further ensuring the face and content validity of the questionnaire.

Data analysis and results

SEM was used for data analysis due to its capacity to estimate numerous interconnected dependence connections based on observable and latent components while accounting for estimation errors (Hair et al., 2011 ). Fornell and Larcker ( 1981 ) provided three criteria to determine the convergent validity of the measurement model: (1) item reliability, (2) the composite reliability (CR) of each construct, and (3) the average variance extracted (AVE). Moreover, to assess item reliability, all item factor loadings were significant and greater than 0.50 (Hair et al., 2005 ). CR measures the internal consistency reliability of a latent construct. A threshold value of 0.70 or higher is often considered to be acceptable, indicating that the measures consistently represent the same latent construct (DeVellis, 2003 ). AVE quantifies the amount of variance hat is captured by a construct relative to measurement error. A threshold value of 0.50 or higher is typically considered to be adequate, suggesting that the construct explains more than 50% of the variance in its indicators (Fornell and Larcker, 1981 ). To evaluate discriminant validity, the square root of a specific construct’s AVE was compared to its correlation with all other constructs. Discriminant validity was deemed acceptable if the square root of the AVE was larger than the correlations.

The constructs should be assessed before evaluating the model fit, thus verifying the reliability and validity of the questionnaire used in the study. The test is evaluated using four indicators: Cronbach’s alpha, CR, AVE, and Variance Inflation Factor (VIF). In a confirmatory investigation guided by mature theories, Cronbach’s alpha should be greater than 0.8, CR larger than 0.7, AVE more than 0.5, and VIF close to 3 or lower (Hair et al., 2009 ). In this study, the values of these indicators were calculated using SPSS 27.0 and AMOS 26.0 software (developed by IBM in Armonk), revealing that Cronbach’s alpha was more than 0.8, CR was greater than 0.7, AVE was higher than 0.5, and VIF was lower than 3 (see Table 3 ). According to the results shown in Table 3 , these claims held for all six constructs, suggesting that the proposed model satisfies the convergent validity criteria.

The validity test is used to assess discriminant validity between variables. A low correlation and a substantial difference between two latent variables are known as discriminant validity. Discriminant validity may be evaluated by comparing the square root of AVE to the correlation coefficient between variables. According to Fornell and Larcker ( 1981 ), a variable has high discriminant validity if the correlation coefficient between it and other variables is less than the square root of the AVE of the variable. The data shown in bold font in the table are the square root of the AVE, which is larger than all the values included in the table in which it is located, as shown in Table 4 . Hence, the discriminant validity of the measurement model included in this study is acceptable and suitable.

Because SEM lacks a separate and powerful evaluation index such as traditional analysis techniques such as ANOVA and regression, it is frequently necessary to compare the covariance matrix of the sample to that of the theoretical model to evaluate its fit effect. AMOS software allows for 25 different types of fit indices, but not all of these indices must be reported. The most frequently reported indicators that measure the fitness of structural equation models are shown in Table 5 . The aforementioned fit indices offer diverse viewpoints regarding the adequacy of the SEM, taking into account multiple dimensions of the model’s intricacy and efficacy. Obtaining a comprehensive assessment of the model fit is often achieved by reporting multiple fit indices.

No fixed standard for fit indices has been universally established. While minimizing the chi-square value is desirable, the influence of sample size expansion on the chi-square value can affect its reference value. Hair et al. ( 2009 ) highlighted the potential significance of the chi-square statistic as sample size increases. With respect to larger sample sizes, even minor deviations in the model can be magnified, leading to a statistically significant chi-square value. However, it is important to note that the significance of the chi-square statistic does not necessarily imply poor model fit. A significant chi-square value should not be automatically interpreted as indicating a lack of adequacy in the model (Hair et al., 2009 ).

Consequently, various indicators based on chi-square values have been developed, and the specific research context also influences the choice of fit index standards. For example, standards for fit indices differ between confirmatory and exploratory research, with exploratory research often employing lower standards than confirmatory research. Furthermore, variations in established standards exist. Therefore, researchers often consult the suggestions of authoritative scholars in the field of structural equations when evaluating model fit (Hayduk, 1987 ; Bagozzi and Yi, 1988 ; Hu and Bentler, 1998 ; Hair Jr et al., 2017 ). Table 5 presents the numerical findings and recommended values for the indices of the proposed model in this study. The goodness-of-fit indices meet the necessary threshold, indicating that the model is well-suited to the provided data based on comparative analysis.

IBM AMOS 26 software was utilized in this research to effectively analyze the data and test the hypotheses within the framework of SEM, thereby enhancing the rigor and accuracy of the study’s findings. In this study, SEM analysis was employed for estimates, as was the validation of the conceptual framework by reference to statistical results and its links to outlier results (Shah and Goldstein, 2006 ). Figure 2 depicts the model after testing all six hypotheses collectively. The regression weights are indicated by the arrows. Table 6 summarizes the hypotheses.

Values on the straight arrows between variables represent the standardised path coefficients.

According to Fig. 2 and Table 6 , the following seven of the ten relationships indicated in the study framework were validated: PU → LA, PEU → LA, SN → LA, PEU → PU, SN → PBC, PU → IABL, and LA → IABL. PU ( β  = 0.301, p  < 0.001), PEU ( β  = 0.291, p  < 0.05), and SN ( β  = 0.324, p  < 0.001) have a positive influence on LA toward blended learning, which explains 62% of the variation. This finding indicates that 62% of the variation in learners’ attitudes variables could be explained by the three independent variables, i.e., PU, PEU, and SN. In addition, PEU ( β  = 0.647, p  < 0.001) has a substantial impact on PU, collectively contributing 41.8% of the variance. Moreover, SN ( β  = 0.711, p  < 0.001) significantly affects PBC, accounting for 50.6% of the variation. Among the factors affecting IABL, only two variables, i.e., PU ( β  = 0.229, p  < 0.01) and LA ( β  = 0.226, p  < 0.05), were significant, explaining 67.6% of the variance of the IABL variable; in contrast, the other three variables, i.e., PEU, SN, and PBC, were not significant.

In conclusion, the study found that PU, PEU, and SN had significant impacts on LA. SN had the greatest impact on LA, with a path coefficient of 0.324. This result indicated that higher levels of subjective norms were associated with higher levels of learning attitudes among Chinese university students. PU and PEU had the second and the third greatest impacts on LA, with path coefficients of 0.301 and 0.291, respectively, which also affected LA toward blended learning to a large extent. According to the data presented above, LA toward blended learning was more influenced by the people around the participants (or the environment in which they were located) in the post-epidemic situation. LA was also affected by this factor and became more positive. In addition, if it was more effective and convenient to engage in blended learning, this situation also improved LA toward blended learning as well as its acceptance. It can thus be concluded that the significance of PU, PEU, and SN with regard to determining LA was also proven in this investigation, echoing the findings of previous technology acceptance research (e.g., Davis et al., 1989 ; Venkatesh and Bala, 2008 ). When the present study’s findings were compared to the data reported by previous studies, it was discovered that the effects of PU and PEU on LA are compatible with the propositions of Lee ( 2010 ). The relationship between SN and LA was also validated. This finding revealed that Chinese university students’ perceptions of social expectations and pressures associated with BL were linked to their overall attitudes toward this learning approach. In other words, when Chinese university students perceive that their peers, instructors, or other influential individuals support or encourage blended learning, these perceptions might positively impact their attitudes and openness toward adopting this approach. This finding highlights the importance of SN in shaping Chinese university students’ LA and acceptance of blended learning. This result suggests that creating a positive social environment that promotes and supports blended learning could have a favorable impact on Chinese university students’ LA and willingness to engage with this learning method.

Moreover, the AMOS analysis indicated that PU and LA had a significant influence on IABL, accounting for 67.6% of the variance in IABL. In contrast, PEU, SN, and PBC had no significant impacts on IABL. The different effects of PU and PEU on IABL can be explained by reference to the two-factor theory proposed by Herzberg, an American behavioral scientist. Monitoring hygiene and motivation variables as per Herzberg’s two-factor theory is a common approach used to determine the elements that drive satisfaction and motivation. In essence, this theory recognizes two sorts of factors: hygiene factors that contribute to student dissatisfaction and motivation factors that contribute to student satisfaction (Herzberg et al. 1959 ). Hygiene factors are critical in preventing dissatisfaction, while motivation factors are essential for promoting actual satisfaction (Herzberg, 1966 ). The former term refers to factors that are dispensable when they exist but cause user dissatisfaction if they do not exist, whereas the latter are factors that contribute to user satisfaction. According to the data collection and analysis, IABL is also driven by two elements, in which context PEU represents a hygiene factor and PU serves as a motivation factor. The PU of blended learning itself is widely considered by students to fall under the general needs of learners with regard to improving their learning, while PEU is a secondary factor. These findings are also compatible with those reported by Oh and Yoon ( 2014 ), indicating that university students may be more familiar with the operation of technology and have high learning capacity because they grew up in the Internet age. According to previous research, PEU, SN, and PBC have little effect on whether learners have IABL, but PU has a more significant impact in this context (Lee, 2010 ; Chen et al., 2012 ). The findings agree with those reported by Dakduk et al. ( 2018 ) and Prasad et al. ( 2018 ), demonstrating that persuasion and the public opinions of their peers, friends, instructors, or others have no impacts on Chinese university students’ involvement in blended learning, which is rather a logical choice on their part. The reasons for this situation are as follows. One possible explanation is that learners prioritize the perceived benefits and advantages of blended learning as superior to other factors. PU is rooted in the TAM and suggests that individuals are more inclined to adopt a technology if they perceive it to be useful with regard to achieving their goals or fulfilling their needs. In the context of blended learning, learners may be motivated by potential benefits such as improved learning outcomes, enhanced access to resources, flexibility in learning, or increased engagement. Thus, when learners perceive blended learning as useful, they are more likely to develop an intention to adopt it. Additionally, this finding may be attributed to the evolving nature of technology and its integration into education. As blended learning continues to gain increasing recognition and prominence, learners might already possess a certain degree of familiarity and comfort with the use of digital tools and platforms. Therefore, the perceived ease of use of blended learning technologies may not be as influential in their decision-making process, as learners may have already overcome initial the usability challenges through their previous experiences with technology in education. Moreover, subjective norms and perceived behavior control might have limited influence due to the complex and individualistic nature of learners' decision-making processes. Because university students are mentally mature, they are conscious of the influence of blended learning on their cognitive thinking capacity and emotional attitudes. The IABL of Chinese university students may be more driven by personal motivations, learning preferences, and perceived self-efficacy than by external social pressures or perceived control over their behavior. Learners’ perceptions of the usefulness of such learning may align more closely with their personal goals and motivations, making this factor a stronger predictor of their intention to adopt blended learning. The present blended learning strategy, on the other hand, is based on top-down promotion, which does not take into account students’ subjective acceptance of the model and is more closely related to school affairs and the faculty. It is important to note that this finding is context-specific to the adoption of blended learning and may not necessarily apply to other educational contexts or technology adoption scenarios. However, from an academic standpoint, this finding provides insights into the factors that influence learners’ IABL and highlights the significance of emphasizing the PU of blended learning when promoting its adoption by Chinese university students.

Finally, to test for the existence of a mediating effect, we performed percentile bootstrapping and bias-corrected percentile bootstrapping (Taylor et al., 2008 ) on 5000 bootstrapped samples with 95% confidence intervals, examining PU, LA and PBC for three mediating variables. We followed the suggestion of Preacher and Hayes ( 2008 ) and calculated the confidence interval of the upper and lower bounds to test whether the indirect effect was significant. Significant summative effects were found in all paths studied. As shown in Table 7 , the results of the bootstrap test confirmed the positive and significant mediating effect of LA in the relationship between PU and IABL (standardized indirect effect of 0.299, p  < 0.001), and PU played a significant mediating role in the relationship between PEU and IABL (the standardized indirect effect was 0.267, p  < 0.001). Therefore, H11 and H12 were supported. PU had a significant indirect positive effect on the IABL of Chinese university students through LA. PU improved the effect of attitudes on IABL. Simultaneously, PEU had a significant indirect positive effect on the IABL of college students through PU. For Chinese university students, more attention is given to the practical utility of blended learning, while less attention is given to the difficulty of participating in blended teaching. If students have strong perceptions of the actual effect of blended learning, these perceptions can promote their positive attitudes toward participating in blended teaching and thus enhance their willingness to accept blended teaching. Similarly, if students perceive the ease of participating in blended teaching more strongly, the effect of improving students’ positive attitudes and willingness to participate in blended teaching is slightly weaker. To effectively implement the blended teaching model, it is essential to prioritize its usability while emphasizing student-centered approaches. By enhancing university students’ learning experiences, the blended teaching model can demonstrate its remarkable practical value, thereby fostering increased acceptance of blended learning among a wider student population. Therefore, the tasks of optimizing student outcomes and creating an engaging learning environment should be a key focus, which can subsequently bolster students’ willingness to embrace blended learning.

Implications

This research has important implications for future studies on how intention indicators may encourage learners to adopt blended learning.

Theoretical implications

As described in section 2, the theoretical grounds for this work were Davis’s ( 1985 ) TAM and the TPB (Ajzen, 1985 ). The TAM was developed as a critical theory to justify and predict user acceptance of technology because it conceptualizes the elements that impact consumers’ adoption of information system technology. Individuals’ attitudes toward technology may explain their usage of technology according to the TPB. The current study contributes to this theoretical framework through an empirical analysis of the questionnaire findings to offer further evidence on blended learning. Similarly, by continuing research into blended learning in social studies classrooms at Chinese colleges, this study scientifically enhances scholarly understanding of this topic. This study is not only theoretically significant but also contributes to the corpus of scholarly and practitioner-based information concerning blended learning among university students.

The TAM and the TPB have been widely employed in technology acceptance research. However, these models have received criticism due to their perceived limitations, which have hindered their ability to effectively explain technology acceptance in academic contexts. This critique stems from their restrictive nature, which has led to an inadequate understanding of the complex dynamics underlying technology acceptance within educational environments. Notably, the close relationship between user-technology interaction and the educational setting highlights the need for a more comprehensive approach (Al-Emran et al., 2018 ). To address the limitations of the TAM and the TPB, researchers have increasingly embraced the integration of these two models. This integration aims to overcome the oversimplification inherent in these models and to develop a more comprehensive framework. The theoretical underpinnings of this research highlight ongoing discussions regarding the suitability of the TAM in educational contexts. The proposed model seeks to establish a cohesive framework by combining the TAM and the TPB to examine the factors influencing the adoption of blended learning in educational environments.

The findings of this study demonstrate that the research methodology employed in this context effectively supports the primary objective of the study. Specifically, the integrated model offers a robust theoretical foundation for explaining the adoption of blended learning. Furthermore, future studies are encouraged to explore the incorporation of additional theories alongside the TAM and the TPB. The Task-Technology Fit (TTF) Theory, the Unified Theory of Acceptance and Use of Technology (UTAUT), the Theory of Self-Regulation (TSR), and the Expectation Confirmation Model (ECM) are suggested as potential theories that can be referenced in future investigations. By incorporating these complementary theories, researchers can gain a more comprehensive understanding of the multifaceted factors that influence technology acceptance in educational contexts. This integrative approach can contribute to advancing knowledge in this field and provide valuable insights into practical applications.

Practical implications

To improve university students’ attitudes toward blended learning and their perceived behavioral control, it is essential to focus on their actual needs. University students are autonomous and independent learners who prioritize their academic performance and cognitive development. Meeting university students’ actual needs requires a combination of course content characteristics and learners’ cognitive development. This approach not only allows knowledge to be transmitted and resources to be provided but also facilitates the cultivation of university students’ innovative and autonomous learning abilities. Hence, it is crucial to promote the blended learning support system, including teaching content, methods, and platform design.

Blended learning places university students at the center and emphasizes the development of their autonomous learning abilities. The effectiveness of online teaching is largely dependent on the design of an online teaching platform with good functions, a user-friendly interface, and abundant resources. Teachers play an important role in this process by summarizing online learning content, providing guidance and motivation, and inspiring university students’ thinking through interaction, discussion, and case analysis, thereby enhancing the interaction and synergy between online and offline learning. Furthermore, the effective implementation of blended learning requires the support of university management in terms of infrastructure, teaching staff, technical personnel, and student preparation. Management support is crucial for implementing blended teaching, and university management staff should play a vital role in providing instructional management and both psychological and emotional support for students. Additionally, it is important to enhance university students’ self-efficacy and development of subjective consciousness. Blended learning combines various modes of learning, such as online, space-time, open, real-time, and community learning. To adjust to blended learning, university students require encouragement and guidance to boost their confidence and initiative with regard to participating in the course. Finally, student-oriented pedagogy should be advocated throughout the learning process, thereby encouraging university students to adopt proactive attitudes toward blended learning.

Limitations and directions for future research

Although this research performed substantial work to investigate the issue in question, the writers did not discuss this topic exhaustively. The study might thus have some drawbacks. First and foremost, because the study was performed in a metropolis, i.e., Guangzhou, the results are not generalizable. This type of quantitative research was performed through a small-scale, monocultural study based on a higher education context in China. University student samples are drawn from distinct contexts and groups. To evaluate the validity of these conclusions more effectively, future scholars are urged and encouraged to employ additional comparable study designs to investigate diverse samples of college students from varied academic backgrounds, such as different university backgrounds, various class sizes, and diverse professional programs. Additionally, because of the cross-sectional nature of the study (i.e., the data collected for the hypotheses were confirmed by distributing questionnaires at a particular moment in time), we could not completely comprehend the underlying dynamics from university students’ perspectives. To overcome this limitation, future studies should employ a longitudinal approach to acquire a more thorough understanding of the dynamics associated with the variables over time. Furthermore, exploring other variables that may influence teacher acceptance of blended learning would be interesting. A qualitative study might be performed in pursuit of the same goal. As proposed by Arora and Saini ( 2013 ), probabilistic neural networks can be used to predict students’ learning achievements in blended learning. The present study focused on students’ opinions, but the perceptions of instructors and the administrative bodies of higher education institutions can also be considered to represent prospective research directions.

Against the backdrop of existing research, the main contributions and innovations of the study lie in its exploration of students’ adoption of blended learning based on the TAM and the TPB from the perspective of Chinese undergraduates for the first time, thereby expanding the integrated theory of the TAM and the TPB. In addition, this paper contributes by providing a deeper knowledge of blended learning, particularly by identifying the path that improves its learning impact on university students in an innovative academic environment. Based on the TAM and TPB perspectives, data were acquired by administering a questionnaire that was completed by university students. According to a statistical study, the paths by which blended learning performance can be improved are that PU, PEU, and SN influence LA and PU affects PEU. Only PU and LA affect IABL. PEU, SN, and PBC, on the other hand, do not affect IABL. Based on these findings, four recommendations for enhancing higher education delivery through blended learning are provided. First, it is necessary to improve the curriculum’s practicability and implement differentiated instruction. Moreover, in cases of limited resources, the faculty is advised to take advantage of the features of Chinese university students to teach them in a manner that depends on both their ability and their differences in various aspects of learning. Furthermore, teachers should focus on PU when designing courses to improve learning efficiency and the effectiveness of blended learning. As a result, a process of promoting blended learning awareness that extends from top administrators to learners should be implemented. Universities can create the majority of the materials and platforms needed to raise awareness of blended learning among teachers and students. Finally, it is essential to promote online functions and expand communication and engagement channels. Effective learning initiatives can influence learners’ attitudes and behaviors; thus, teachers must remain up to date on current topics to ensure that they can adjust their teaching approaches to fulfill learners’ needs in the future.

Data availability

The datasets analyzed during the current study are available in the Dataverse repository: https://doi.org/10.7910/DVN/PBJ2GY .

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Acknowledgements

This research was supported by the National College Student Innovation and Entrepreneurship Training Program of China (Grant No. 202210337027, No. 202210337003), the College Student Innovation and Entrepreneurship Training Program of Zhejiang University of Technology (Grant No. 2020011) and Higher Education Research Project of the “14th Five-Year Plan” of Guangdong Association of Higher Education in 2022 (Grant No. 22GYB158).

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Conceptualization: T.Y. Methodology: T.Y., C.W. Software: C.W. Writing—original draft preparation: T.Y., C.W. Writing—review and editing: T.Y., J.D., C.W. Supervision: J.D. Project administration: T.Y. Funding acquisition: T.Y. All authors read and approved the final manuscript. All authors have read and approved the re-submission of the manuscript.

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Yu, T., Dai, J. & Wang, C. Adoption of blended learning: Chinese university students’ perspectives. Humanit Soc Sci Commun 10 , 390 (2023). https://doi.org/10.1057/s41599-023-01904-7

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Blended Learning: Research Perspectives, Volume 3 offers new insights into the state of blended learning, an instructional modality that combines face-to-face and digitally mediated experiences. Education has recently seen remarkable advances in instructional technologies such as adaptive and personalized instruction, virtual learning environments, gaming, analytics, and big data software. This book examines how these and other evolving tools are fueling advances in our schools, colleges, and universities. Original scholarship from education’s top thinkers will prepare researchers and learning designers to tackle major issues relating to learning effectiveness, diversity, economies of scale, and beyond.

TABLE OF CONTENTS

Section section i | 29  pages, introduction and foundations, chapter 1 | 7  pages, introduction, chapter 2 | 20  pages, exploring definitions, models, frameworks, and theory for blended learning research, section section ii | 63  pages, student outcomes, chapter 3 | 16  pages, neotraditional students and online discussions, chapter 4 | 24  pages, blended delivery modes and student success, chapter 5 | 21  pages, scaling course design as a learning analytics variable, section section iii | 50  pages, faculty issues, chapter 6 | 19  pages, highly effective blended teaching practices, chapter 7 | 15  pages, blended faculty community of inquiry transforms online teaching perceptions and practices, chapter 8 | 14  pages, impact analysis of ten years of blended learning, section section iv | 45  pages, adaptive learning research, chapter 9 | 16  pages, efficacy of adaptive learning in blended courses, chapter 10 | 15  pages, adaptive and active, chapter 11 | 12  pages, a blended learning case study, section section v | 75  pages, k–12 perspectives, chapter 12 | 21  pages, competencies and practices for guiding k–12 blended teacher readiness, chapter 13 | 17  pages, examining peer-to-peer supports in k–12 blended academic communities of engagement, chapter 14 | 16  pages, intellectual agency of linguistically diverse students with disabilities in a blended learning environment, chapter 15 | 19  pages, multimodal blended learning and english language learners, section section vi | 46  pages, international perspectives, chapter 16 | 13  pages, negotiating the blend, chapter 17 | 18  pages, blended learning and shared metacognition, chapter 18 | 13  pages, evidence-based blended learning design, section section vii | 49  pages, science and health research, chapter 19 | 18  pages, blending geoscience laboratory learning and undergraduate research with interactive open educational resources, chapter 20 | 16  pages, student experiences learning psychomotor skills in a blended doctor of physical therapy program, chapter 21 | 13  pages, integrative blended learning, section | 40  pages, chapter 22 | 38  pages, education and blended learning.

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Blended Learning Research in Higher Education and K-12 Settings

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• First Online: 15 October 2023
• pp 3107–3135
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• Lisa R. Halverson 4 ,
• Kristian J. Spring 4 ,
• Sabrina Huyett 5 ,
• Curtis R. Henrie 4 &
• Charles R. Graham 4  

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Blended learning is adopted widely in educational settings. Over the past decade, blended courses have increased in higher education (HE), and currently blended learning is expanding in K-12 as well. As blended learning becomes more prevalent, opportunities for research into blended learning are also increasing. Researchers and practitioners need to know the current issues and lines of inquiry prominent in blended learning to direct them to the cutting-edge research and enable them to identify the most pressing problems. This chapter synthesizes and categorizes current blended learning research, with recommendations for future directions. Issues addressed in HE blended learning and K-12 blended learning are identified, compared, and evaluated by reviewing major research on the topic. Finally, future research steps and important research gaps are described.

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Halverson, L.R., Spring, K.J., Huyett, S., Henrie, C.R., Graham, C.R. (2023). Blended Learning Research in Higher Education and K-12 Settings. In: Spector, J.M., Lockee, B.B., Childress, M.D. (eds) Learning, Design, and Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-17461-7_31

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Revenue growth management: Building capabilities to sustain impact

Key takeaways.

• In the current inflationary environment, revenue growth management (RGM) has emerged as a top priority for consumer leaders, and companies that do it well typically see a significant gain in annualized gross margins of 4 to 7 percent.
• To sustain impact over time, companies must embed RGM cross-functionally and invest in capability building as they are 2.4 times more likely to capture and sustain value from it.
• At-scale RGM capability building is at the top of the consumer agenda and must incorporate four key principles to do it well.

The growth of online shopping, supply chain constraints, shifts in the channel mix, gross margins strained by rising inflation—these are just a few of the headwinds consumer companies have faced over the past several years. Not surprisingly, revenue growth management (RGM) has emerged as a top priority for consumer leaders in this environment. 1 “ Revenue growth management: The time is now ,” McKinsey, August 10, 2021. Companies that do it well have been better able to meet consumer expectations and align pricing strategies across channels, which typically has led to a 4 to 7 percent gain in annualized gross margins. 2 This estimate is based on McKinsey analysis of numerous companies’ annual reports.

About the authors

This article is a collaborative effort by Katie Finnegan, Subhen Jeyaindran, Sheldon Lyn , Mher Panossian, and Roman Steiner, representing views from McKinsey’s Growth, Marketing & Sales Practice.

But to sustain such gains over time, businesses should consider embedding RGM best practices across their organization. That requires several actions, including establishing the right global or regional and local organization structures, hardwiring revenue growth techniques into core business processes, implementing incentives, such as integrating RGM into performance scorecards, and—critically—building and maintaining these capabilities at scale. Indeed, companies that invest in capability building are 2.4 times more likely to capture and sustain value. 3 Scott Keller and Colin Price, Performance and Health: An evidence-based approach to transforming your organization , New York, NY, McKinsey & Company, 2010.

The necessary capabilities encompass skills, behaviors, mindsets, and techniques to support and further expand the company’s RGM strategy and approach in areas that include pricing, promotions, assortment, and trade investment. While the exact proficiencies will vary across companies and roles, from sales and marketing to finance and supply chain, the core RGM capabilities for sustained impact remain the same. At scale, they provide organizations with enterprise-wide muscle to manage, react to, and meet such challenges as the current rise in inflation. Without this scale, the RGM effort will likely amount to a one-time initiative led by a few experts in central positions and leave companies unguarded when new challenges arise.

This has put at-scale RGM capability building at the top of the consumer agenda. It may not be easy to do, but there are principles that have worked well at best-in-class organizations.

Would you like to learn more about our Growth, Marketing & Sales Practice ?

Hallmarks of great rgm capability building.

Companies commonly face four distinct challenges in strengthening the skill sets, competencies, and strategies for successful management of revenue growth. First, RGM proficiency levels often vary across the organization; there is not typically a baseline of knowledge, and experts with the needed skills are unevenly distributed. Second, broad scaling is hard because knowledge is often centralized among a few individuals on the core RGM team, and as needs shift with digital advances, new customer behaviors, and the changing post-COVID-19 economy, these experts cannot disseminate their knowledge fast enough to keep up. 4 “ What got us here won’t get us there: A new model for the consumer goods industry ,” McKinsey, July 30, 2020. Third, stakeholders beyond the commercial functions need to be part of the training curriculum to unlock RGM’s cross-functional value. Finally, people often feel they don’t have time for skills-building classes in addition to their other responsibilities.

To successfully build and maintain the capabilities that sustain an RGM effort, companies need to plan for and resolve these challenges. Our experience suggests that there are five hallmarks of successful RGM capability building.

Start from the current baseline of RGM knowledge and skills

Capability building is a big commitment for an organization and its people, and knowing where to focus the program is pivotal to designing and delivering a relevant, impactful learning journey. Therefore, taking time to identify the current levels of knowledge and skills—for example, through an assessment of individual skills across all key RGM concepts—is a good starting point, as it will allow leaders to understand the greatest gaps and align the right set of learning outcomes with the right people. Leading companies perform granular baseline assessments to determine all RGM subskills and analyze data by role, tenure, region, and the like.

Be cross-functional and collaborative

Successful RGM teams work cross-functionally and use a common RGM-specific vocabulary and acronyms across departments. This is particularly important given that relevant employee groups stretch across many parts of an organization, from key account managers (KAMs) to finance. Cross-functionality helps maximize the value of RGM capability building as the whole organization becomes aligned on the effort. For example, supply chain stakeholders can work with finance departments to change the pack-price architecture of certain products and can continue innovating on commercial renovations using an RGM mindset (Exhibit 1).

Consider a food company where leaders were working to instill a company-wide RGM approach and an accompanying common language. Employees in need of training did not have a lot of time available to attend learning sessions, however. So leaders focused on involving a broad set of stakeholders in their RGM capability-building programs—from supply chain and innovation teams to commercial teams, such as KAMs and brand managers. Program attendance was encouraged by company leadership in an effort to role model and reinforce the importance of the overall training and the participation of local leaders in each deep-dive workshop. Leaders made it clear that this was a time for everyone to step away from their desk, put down the phone, and roll up their sleeves to learn and apply RGM best practices. To maximize the value of the program for participants, the company integrated data from past experiences and real-life challenges into the workshop exercises. Leaders used those as learning opportunities to advance RGM initiatives, discuss challenges cross-functionally, and consider potential solutions.

Revenue growth management: The time is now

Tailor learnings to proficiency levels.

While everyone in the organization will benefit from day-to-day RGM knowledge, such as price and promotion management and trade analytics, different roles and stakeholders require different levels of proficiency across core RGM capabilities. KAMs, for example, need to be quite proficient at implementation, so it will have more emphasis during their learning journeys. Leaders should categorize the different levels of RGM proficiency, from basic understanding to advanced to expert, and segment the audience according to their category or proficiency level (Exhibit 2). For example, leaders from departments such as supply chain will need less proficiency in RGM than, say, overarching RGM team because that is not their core function. Based on the baseline assessments, leaders will identify the gaps between people’s current capabilities and those needed for their role in managing revenue growth.

Learning experts can work with RGM leaders to tailor the training curriculum to groups at each proficiency level and design distinct journeys for different departments to show how RGM affects and can be integrated into their day-to-day jobs. Organizations that do this have an easier time sustainably scaling up capability building across various roles.

A personal care company was rolling out a multicountry transformation of business unit RGM, but it was struggling to gain traction in implementing initiatives. RGM was not part of business as usual in every market, and many local teams did not fully understand the RGM analytics and insights and therefore didn’t feel compelled to implement the recommendations. The company decided to segment the training by targeting proficiencies for each role. Experts who were fully trained in RGM capabilities were given responsibility for incorporating tools and concepts into day-to-day work in each country. They trained KAMs and trade-marketing managers to an advanced proficiency level so that they could use the RGM tools to gain insights daily, while the wider organization received a broad training to bring everyone up to basic proficiency. This approach has not only helped the company scale learnings faster but has also provided a valuable tool for reinforcement.

Train experts to scale learnings

Scaling any capability across an organization is hard; this is especially true for RGM, given that it is highly analytical, strategic, and a comparably newer muscle to strengthen. As such, it should be taught by experienced facilitators, who are not usually available in large numbers. A train-the-trainer approach, then, is often most successful. In this approach, the most proficient—for example, the central RGM team—teach the next level about how to amplify RGM in specific departments. This creates a pool of trained facilitators who can, in turn, train new employees on these concepts to build a continuous loop of knowledge across present and future faculty. Eventually, this approach builds a cadence to teach and practice RGM skills across the entire organization. This system also allows the company to easily scale learning, even as roles change and new people are hired, and companies often develop a playbook to serve as a resource guide to drive reinforcement.

At a consumer durables company, for example, leaders saw a need to broadly roll out RGM capabilities but did not yet have a clear group of experts in place. Concurrently, big shifts were happening in the industry, such as the rise of online channels, which further highlighted the need for RGM capabilities. To both build an expert knowledge base and develop a continuous RGM learning loop, the team used the train-the-trainer approach to develop a preliminary cohort of leaders who could then serve as subject matter experts to teach future cohorts. The approach allowed the company to scale to a faculty pool of 15 people globally in just two months. This first group of RGM champions had the opportunity to shape the program’s delivery and put their personal touch on the RGM capability-building experience.

Harness different learning techniques

Our experience indicates that capability building is most effective when there is the right balance of theory and application—that is, when employees learn RGM concepts and then put them into practice. This is called the field-and-forum approach. Learners spend approximately 70 percent of their time on fieldwork, such as individual assignments and lighthouse projects, 20 percent receiving feedback through coaches and peers, and 10 percent attending workshops and taking courses (Exhibit 3).

Advances in behavioral science that inform adult-learning approaches

Five new technologies and advances in behavioral science 1 Encompasses research across multiple domains, such as neuroscience, behavioral economics, sociology, and decision science. can be leveraged for blended learning journeys that maximize education and sustain change:

Measurement : embedding feedback loops into existing systems and processes to support the most important lessons

Digital learning : using technology, such as computers or mobile devices, to allow learners to consume just-in-time, self-paced learning

Nudges : embedding strategically designed interventions—for example, through emails, notifications, or text messages—that guide choices, influence behavior, and increase engagement

Personalization : crafting capability programs that facilitate self-directed learning tailored to individual needs

Microlearning : incorporating content that is short, relevant, contextualized, and targeted

Leading companies harness behavioral science to design an immersive learning journey that integrates RGM theory. They also allow faculty to practice new concepts using case examples, provide real world on-the-job learning opportunities, and set a cadence of refresher trainings to help learners acquire, apply, and sustain learning (see sidebar, “Advances in behavioral science that inform adult-learning approaches”).

For example, leaders at a food company fully integrated the learning journey into participants’ daily jobs. The journey featured on-the-job learnings, such as reviews of RGM promotion plans that were embedded into the regular KAM planning cycle, as well as bootcamps that provided an immersive learning experience. More than 70 practitioners were trained and certified in RGM globally across four markets with a participation rate of more than 90 percent throughout the full journey.

Advanced capabilities in RGM are only increasing in relevance as the macroeconomic, market, retailer, and competitive landscapes continue to evolve and intensify. Companies across the globe have a unique opportunity to reset their RGM strategies to get ahead. RGM capability building is not a one-and-done effort. RGM leaders and consumer executives must assess whether they have the right capabilities to capture opportunities today and the ability to develop those they will need for the future.

Katie Finnegan is a consultant in McKinsey’s Chicago office; Subhen Jeyaindran is a consultant in the New Jersey office, where Mher Panossian is an associate partner; Sheldon Lyn is a partner in the Southern California office; and Roman Steiner is a partner in the Zurich office.

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K-12 Blended E-Learning Market size is set to grow by USD 20.75 billion from 2024-2028, Need for cost-effective teaching model to boost the market growth, Technavio

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Jun 24, 2024, 17:30 ET

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NEW YORK , June 24, 2024 /PRNewswire/ -- The global K-12 blended E-learning market  size is estimated to grow by USD 20 .75 billion from 2024-2028, according to Technavio. The market is estimated to grow at a CAGR of  14.07%  during the forecast period. Need for cost-effective teaching model is driving market growth, with a trend towards emergence of learning via mobile devices. However, increase in open-source learning content  poses a challenge. Key market players include Apollo Asset Management Inc., Articulate Global Inc., Cisco Systems Inc., Coursera Inc., D2L Inc, Docebo Inc., Educomp Solutions Ltd., edX LLC, Ellucian Co., Houghton Mifflin Harcourt Co., Instructure Holdings Inc., Pearson Plc, PowerSchool Holdings Inc., Promethean World Ltd., Providence Equity Partners LLC, Samsung Electronics Co. Ltd., Scholastic Corp., Stride Inc., Toppr Technologies Pvt. Ltd., and Vedantu Innovations Pvt. Ltd..

Get a detailed analysis on regions, market segments, customer landscape, and companies - Click for the snapshot of this report

Key Market Trends Fueling Growth

In the K-12 education sector, the integration of mobile devices in blended e-learning is becoming essential. Smartphones and tablets are no longer just tools for content consumption but also serve as platforms for attending lectures, receiving alerts, and participating in educational activities. With the rise of tech-savvy students and growing awareness of mobile device uses, these devices have become indispensable in the education services market. The blended e-learning approach combines online and offline learning, making mobile devices a crucial component for accessing content. As a result, the adoption of mobile learning is expected to expand significantly in the coming years. 

The K-12 Blended E-Learning market is currently experiencing significant growth. Technology and classes are coming together to create effective learning environments. The use of virtual classrooms and learning management systems is on the rise. Classrooms are becoming more interactive with the integration of multimedia content and real-time assessments. The trend towards personalized learning is also gaining momentum. Devices like laptops and tablets are being used to deliver instruction and engage students. The use of artificial intelligence and machine learning is making learning more efficient and effective. The future of K-12 education is a blend of traditional and digital learning. 

Research report provides comprehensive data on impact of trend. For more details-  Download a Sample Report

Market Challenges

• In the K-12 blended e-learning market, open-source solutions like MOOCs pose a significant challenge to vendors. These platforms offer free access to a vast user base, making them popular among students. Khan Academy and Byju's are notable providers of free courses in various subjects. MOOCs' affordability and holistic approach appeal to users, leading some to prefer them over paid versions for specific online courses. Edx and Coursera also offer free or low-cost courses. These trends underscore the importance of cost-effective and comprehensive offerings in the K-12 blended e-learning sector.
• The K-12 Blended E-Learning Market faces several challenges in implementing and integrating technology for effective education. One challenge is ensuring equal access to technology for all students, especially those from disadvantaged backgrounds. Another challenge is the lack of consistent and high-quality digital content. Additionally, training teachers to effectively use technology in the classroom is a significant hurdle. Furthermore, ensuring student engagement and reducing the digital divide between home and school learning are ongoing concerns. Lastly, ensuring data security and privacy is crucial in the implementation of blended learning solutions.

For more insights on driver and challenges -   Download a Sample Report

Segment Overview 

This k-12 blended e-learning market report extensively covers market segmentation by

• Product  
• 1.1 Hardware
• 1.2 Content
• 1.4 Solutions
• Application  
• 2.1 Pre-primary school
• 2.2 Primary school
• 2.3 Middle school
• 2.4 High school
• Geography  
• 3.1 North America
• 3.4 South America
• 3.5 Middle East and Africa

1.1 Hardware-  The K-12 Blended E-Learning Market is experiencing significant growth due to the adoption of affordable hardware devices such as laptops, tablets, IWBs, and LCS from vendors like Samsung Electronics, Educomp Solutions, and Providence Equity Partners. Interactive digital displays and wearable devices are emerging trends, enhancing the teaching and learning experience. BYOD and mobile learning are common practices, especially in the higher secondary segment. However, challenges such as bandwidth concerns, capacity, access points, security, and authorization need to be addressed for seamless implementation.

For more information on market segmentation with geographical analysis including forecast (2024-2028) and historic data (2018 - 2022)  - Download a Sample Report

Research Analysis

In the dynamic and evolving K-12 Blended E-Learning Market, students and teachers in the Global Economy are embracing advanced technologies to enhance classroom instruction. The EdTech sector is thriving, with the Hardware and Content segments driving innovation. Blended e-learning, a combination of traditional and virtual methods, is gaining popularity due to its cost-effectiveness and ability to connect students across G21 jurisdictions. Digital education solutions, such as Artificial Intelligence, Virtual Reality, and Augmented Reality, are revolutionizing the learning experience. Online courses and Gamification are engaging students, while Personalization in learning ensures a high Quality of content. However, the lack of standardization poses a challenge to the E-Learning market's growth. Silicon Valley tech firms continue to invest in these solutions, ensuring continuous advancements in the field.

Market Research Overview

The K-12 Blended E-Learning Market refers to the integration of technology into traditional classroom teaching methods. This approach combines the benefits of face-to-face instruction with online learning. The use of digital tools and resources enhances student engagement and improves learning outcomes. The market for blended e-learning in K-12 education is growing rapidly due to the increasing availability of high-speed internet and the need for personalized learning experiences. The curriculum is delivered through a combination of synchronous and asynchronous methods, allowing students to learn at their own pace. The use of multimedia content, interactive activities, and assessments facilitates a more interactive and engaging learning experience. The market for blended e-learning in K-12 education is expected to continue growing as schools seek to provide students with the necessary skills for the digital age.

Table of Contents:

1 Executive Summary 2 Market Landscape 3 Market Sizing 4 Historic Market Size 5 Five Forces Analysis 6 Market Segmentation

• Application
• Pre-primary School
• Primary School
• Middle School
• High School
• North America
• South America
• Middle East And Africa

7 Customer Landscape 8 Geographic Landscape 9 Drivers, Challenges, and Trends 10 Company Landscape 11 Company Analysis 12 Appendix

About Technavio

Technavio is a leading global technology research and advisory company. Their research and analysis focuses on emerging market trends and provides actionable insights to help businesses identify market opportunities and develop effective strategies to optimize their market positions.

With over 500 specialized analysts, Technavio's report library consists of more than 17,000 reports and counting, covering 800 technologies, spanning across 50 countries. Their client base consists of enterprises of all sizes, including more than 100 Fortune 500 companies. This growing client base relies on Technavio's comprehensive coverage, extensive research, and actionable market insights to identify opportunities in existing and potential markets and assess their competitive positions within changing market scenarios.

Technavio Research Jesse Maida Media & Marketing Executive US: +1 844 364 1100 UK: +44 203 893 3200 Email:  [email protected] Website:  www.technavio.com/

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Blended learning in physical education: A systematic review

Associated data.

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

This review aims to provide a detailed overview of the current status and development trends of blended learning in physical education by reviewing journal articles from the Web of Science (WOS) database. Several dimensions of blended learning were observed, including research trends, participants, online learning tools, theoretical frameworks, evaluation methods, application domains, Research Topics, and challenges. Following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), a total of 22 journal articles were included in the current review. The findings of this review reveal that the number of blended learning articles in physical education has increased since 2018, proving that the incorporation of online learning tools into physical education courses has grown in popularity. From the reviewed journal articles, most attention is given to undergraduates, emphasizing that attention in the future should be placed on K-12 students, teachers, and educational institutions. The theoretical framework applied by journal articles is also limited to a few articles and the assessment method is relatively homogeneous, consisting mostly of questionnaires. This review also discovers the trends in blended learning in physical education as most of the studies focus on the topic centered on dynamic physical education. In terms of Research Topics, most journal articles focus on perceptions, learning outcomes, satisfaction, and motivation, which are preliminary aspects of blended learning research. Although the benefits of blended learning are evident, this review identifies five challenges of blended learning: instructional design challenges, technological literacy and competency challenges, self-regulation challenges, alienation and isolation challenges, and belief challenges. Finally, a number of recommendations for future research are presented.

1. Introduction

The integration of multiple technologies into traditional instruction has attracted enormous attention and offered numerous research avenues over the years. For instance, influential studies have confirmed the benefits of blended learning. According to Müller and Mildenberger ( 1 ), the definitions of blended learning most commonly used in scientific publications are those by Graham [( 2 ), p. 5]: “blended learning is a combination of face-to-face and computer-mediated instruction” and by Garrison and Kanuka ( 3 ): “thoughtfully integrate the face-to-face learning experience in the classroom with the online learning experience.” Therefore, blended learning in this review includes technology-supported learning with the exception of fully online and fully face-to-face instruction. According to the sequence of integrating traditional classroom-based and online instruction, blended learning can be classified in the forms of blended, hybrid, flipped, or inverted. Despite the forms of blended learning, the use of blended learning has greater potential for transferring content into practice ( 4 ) and improves the quality and quantity of interaction between teachers and students ( 1 ), flexibility ( 5 ), learning engagement ( 6 ), and differentiated instruction ( 7 ) in classrooms.

To date, blended learning models are considered to be the most widely adopted instructional model by educational institutions as they are regarded as effective in providing flexible, timely, and continuous learning ( 8 ). The models have proven to be an upgrade from traditional learning models and fully online learning models as blended learning models combine the advantages of online and face-to-face learning ( 9 ). As a result, blended learning approach is referred to as the “new traditional model” or the “new normal” due to its advantages in optimizing the teaching and learning ( 10 ).

The significance of physical education in contemporary schooling is recognized internationally. Yang et al. ( 11 ) note that in addition to motor skills and physical fitness, physical education has a positive impact on students in several dimensions, such as their personal and social skills, patience, self-esteem, and self-confidence ( 12 – 14 ). In traditional teaching models of physical education, students are placed in a relatively passive position in order to receive knowledge and skills provided by the curriculum and the teaching content is inflexible as it ignores student differences and limits the opportunities for individual instruction and remediation by teachers ( 15 , 16 ). To address the issue with the traditional teaching models of physical education, López-Fernández et al. ( 17 ) suggest blended learning models to provide students with personalized learning opportunities to optimize the quality of their learning in physical education classes, as well as to motivate students to learn.

A systematic review is necessary to understand current research situations of blended learning in physical education. Even though there have been considerable studies on blended learning in physical education, a systematic review of blended learning in this field is limited. To date, only one systematic review investigating the effectiveness of blended learning in higher physical education has been published ( 18 ). Therefore, this study aims to synthesize and analyze the findings to describe the current state and research trend of blended learning in physical education, and thus establish new directions for future research. This study was driven by the following research questions:

• What are the research trends in blended learning in physical education?
• Who are the main participants?
• What are the main online learning tools?
• What are the theoretical frameworks and evaluation methods used in blended learning in physical education?
• What are the application domains and Research Topics involved in blended learning in physical education?
• What are the reported challenges of blended learning in physical education?

2. Methodology

2.1. search process.

This systematic review follows the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) ( 19 ). The search on the Web of Science (WoS) electronic database for the articles began in July 2022 and concluded in August 2022. WoS electronic database was chosen because of its high reputation and reliability in investigating leading articles. A search string was developed according to researchers' understanding and knowledge in the field of blended learning and physical education, as well as relevant blended learning and physical education search strings reported in other studies such as in Rasheed et al. ( 8 ) and Yang et al. ( 11 ). The search strings: (blended learning OR blended course OR hybrid learning OR hybrid course OR flipped learning OR flipped learning OR flipped classroom) AND (physical education OR sport * OR physical activity * OR exercise), were inserted in the advanced search query of the Web of Science database. The field option was then specified as a topic and restricted the search to the Social Sciences Citation Index. Then, the references of the papers included in this study were reviewed to ensure that the selected papers answered the six research questions of this review.

2.2. Eligibility criteria

To be considered for inclusion in this review, selected journal articles had to meet the following criteria: (a) define blended learning as the incorporation of traditional face-to-face and online learning, (b) related to blended learning in sports or physical education, (c) empirical study of SSCI indexing, and (d) published in English. On the other hand, the exclusion criteria included: (a) articles with sole concern on the face-to-face portion of blended learning, (b) book chapter reviews, meeting abstracts, reports, and review articles, (c) non-English articles, and (d) unavailable full-text articles.

2.3. Study selection

A total of 531 journal articles were identified from the Web of Science database. A total of 256 duplicate articles were removed after considering the articles following the inclusion and exclusion criteria. Then, using the EndNote reference management software, a database of 135 articles with their titles, abstracts, and full text was created. The articles were carefully read and 22 articles were found pertinent to this review. Figure 1 shows the filtering process of this review based on the PRISMA statement ( 19 ).

A review process based on the PRISMA statement.

2.4. Data extraction and quality assessment

The data extraction process included the identification of (a) the article's author, nationality, and publication year, (b) participants (i.e., K-12 students, undergraduates, teachers, and others), (c) online learning tools (i.e., learning platforms, learning software, recorded lectures, online learning materials, and others), (d) theoretical frameworks and evaluation methods (i.e., interviews, questionnaires, tests, and other methods), (e) application domains (i.e., basketball, football, badminton, and other courses) and Research Topics (i.e., perceptions, satisfaction, learning effects, and other items), and (f) challenges.

As the reviewed articles differed in research design, a quality assessment tool developed by Rowe et al. ( 20 ) that has been proven to be a useful tool for assessing qualitative, quantitative, and mixed methods was utilized ( 21 ). The tool assesses five important methodological aspects of a study, namely the background or literature review, sample, study design or methodology, outcome measures, and conclusions (see Table 1 ). The total score ranges from 0 to 5, with the higher scores representing better methodological quality. Articles scoring 4 or 5 are considered to be high in quality, articles scoring 3 are considered to be of moderate quality, and studies scoring between 0 and 2 are considered to be low in quality. In this review, two trained reviewers independently assessed the quality of the article, with disagreements resolved by the third reviewer. All 22 articles received a score between 4 and 5, indicating their high methodological quality.

Methodological quality assessment tool.

* Total score = sum of individual scores.

This part reports the current state of blended learning in physical education and the key findings by addressing the six research questions of this review. The summary of the characteristics of the 22 studies involved is shown in Table 2 .

Characteristics of the studies examined in the preset review.

3.1. Research trends

The first article on blended learning in physical education was published in 2011. However, since then, the research in this field was limited with zero publications in 2012, 2013, 2014, 2015, and 2017, and only one publication in 2016. However, beginning in 2018, physical education researchers have become increasingly interested in blended learning, with the number of articles reaching a peak in 2020. Journal articles published before August 2022 were also included. However, the number did not represent the accurate situation for the entire year of 2022 because this review concluded in August 2022. The graph of the trends in research on blended learning in physical education is shown in Figure 2 .

Number of articles published by year.

Based on the number of publications on blended learning in physical education from 2011 to 2022, studies conducted in China accounted for 41 per cent of the total number of publications ( n = 9). From the nine studies, Lin, Hsia, and Hwang authored five studies ( 27 , 29 , 30 , 36 , 39 ). The next highest number of publications on blended learning in physical education was conducted in Spain ( n = 6) and the United Kingdom ( n = 3), while each of the remaining studies was conducted in countries such as the United States, Singapore, Australia, and Ireland.

3.2. Participants

This review identified a total of 3,543 subjects enrolling in the 22 reviewed articles, with 2 ( 34 ) to 602 participants in each study ( 22 ). It is found that the majority of research subjects were undergraduate students ( n = 15). A total of 5 articles reported detailed information about the majors of their participants and the locations of their degree programs, namely undergraduates of exercise science from Griffith University ( 23 ), undergraduates of physical education from the University of Granada, Organization of Educational Centers (Degree) ( 24 ), undergraduates of Pablo de Olavide University, Physical Activity and Sports Science (Degree) ( 25 ), undergraduates of sports management from San Antonio de Murcia University and Pablo de Olavide University ( 37 ), and undergraduates of sport and exercise science from the Edge Hill University ( 41 ). Out of the 22 reviewed articles, 3 articles focused on teachers, 1 article focused on teachers and undergraduates ( 37 ), and 3 articles focused on K-12 students. Among K-12 students, only primary and secondary students were included ( 32 , 33 , 35 ).

3.3. Learning tools

A variety of learning tools were used in the blended learning activities of physical education. Nine journal articles focused on learning platforms, such as Moodle, Wisdom Master Pro, TronClass, and Superstar as learning tools. Online learning materials, including online lectures, online documents, and online websites were studied in six articles. Learning software was mentioned in three articles, while one article used recorded lectures as the primary learning tool. Also, there were articles combining two learning tools ( 32 , 34 ). The use of a learning platform and robots as learning tools was also studied in an article ( 35 ). Nevertheless, four articles did not report any learning tools.

3.4. Theoretical frameworks and evaluation methods

Blended learning is a pedagogical framework based on multiple theories of teaching and learning. This review discovered that the theories presented in the articles include self-determination theory (SDT) ( 22 , 31 ), WSQ-based flipped learning model ( 29 ), ARQI-based flipped learning model ( 30 ), constructivism theory ( 34 , 37 ), hybrid learning theory ( 35 ), post-humanism theory ( 37 ), cognitive apprenticeship and reflective practice theory ( 36 ), ICRA-based flipped learning model ( 39 ), and 3C model ( 42 ). However, of the 22 articles included in this review, 12 articles did not report a theoretical framework that was used to guide their research and teaching practice.

In terms of evaluation methods, 11 articles on blended learning in physical education used only 1 assessment method, 5 articles used 2 assessment methods, and 6 articles used 3 or more assessment methods. Questionnaires were employed by the greatest number of articles ( n = 15), with 3 of them open-ended questionnaires ( 23 , 25 , 41 ). The evaluation methods were followed by tests ( n = 11) and interviews ( n = 10). Other evaluation methods such as lesson observation, field notes, document analysis ( 34 ), and reflective blogging ( 37 ) were also used.

3.5. Application domains and research topics

The range of applications for blended learning in physical education was diverse. There were 10 articles involving sports courses such as the Physical Activity and Wellness course ( 22 ), Sports Coaching course ( 23 ), and Sports Management course ( 40 ). There were also two articles on theory courses ( 37 , 42 ). In addition, most of the current blended learning articles explored dancing ( 27 , 29 , 30 , 38 ), followed by basketball ( 28 , 31 ), football ( 26 ), Wushu ( 35 ), billiards ( 36 ), and badminton ( 39 ). A total of seven articles did not refer to specific areas of the physical education ( 17 , 24 , 25 , 32 – 34 , 41 ).

This review discovered that many articles investigated more than one Research Topic, and the totals exceeded the number of reviewed articles. As a result, the current review grouped the Research Topics of the 22 articles on blended learning in physical education into seven categories. The first category is the perceptions of students or teachers. This topic was investigated in 13 articles and was the most important concern of the blended learning community. The second category was the effects of blended learning in physical education on student learning. This topic was investigated in 12 articles. A total of 6 investigated the third category of blended learning in physical education which is student satisfaction with blended learning. In addition, 4 articles examined the student motivation ( 22 , 29 , 33 , 36 ) and self-efficacy ( 27 , 30 , 36 ), while 2 articles studied task load. Other Research Topics such as attendance ( 24 ), self-assessment tools ( 25 ), skills qualifications and career development ( 26 ), psychological needs ( 33 ), learning interest and attitude ( 35 ), and interaction ( 40 ) were also discovered.

3.6. The challenges of blended learning in physical education

This review identified five categories of challenges of blended learning in physical education. They were instructional design challenges, technological literacy and competency challenges, self-regulation challenges, alienation and isolation challenges, and belief challenges (see Table 3 ). First, instructional design challenges ( n = 6) involved a set of challenges related to scientific planning and rationalization of all aspects of the teaching and learning process in advance, based on student learning characteristics and teacher teaching styles. The second category was technological literacy and competency challenges ( n = 5), which relates to a range of challenges associated with student/teacher proficiency and competence in the appropriate use of technology for teaching and learning. The third category, self-regulation challenges ( n = 2) involved a series of related student behaviors that prevent students from self-regulating the emotions, thoughts, and actions they plan to take in achieving their learning goals. Belief challenges ( n = 2) included negative attitudes and perceptions of teachers or students about the use of technology for teaching or learning. Finally, alienation and isolation challenges ( n = 1) involved a set of associated emotional discomforts suffered by teachers or students when teaching or learning outside of traditional classrooms, mainly caused by loneliness and isolation from others.

The challenges of blended learning in physical education.

4. Discussion

4.1. summary of findings and discussion.

In this systematic review of the adoption of blended learning in physical education, 22 journal articles retrieved from the Web of Science (WOS) database were analyzed and grouped according to research trends, participants, learning tools, theoretical framework, evaluation methods, application domains, Research Topics, and challenges. The publication trend shows that there has been a growing interest in blended learning in physical education since 2018. This indicates that researchers have recognized the role of technology in physical education and have sought to apply technology in physical education to meet student educational needs based on the current challenges and technological teaching resources offered by contemporary society ( 32 ). In addition, the paucity of high-quality literature suggests that research on blended learning in physical education is still in its infancy around the world. Of the 22 articles in this review, 9 were conducted in China, 6 in Spain, and 3 in the UK. Each of the other articles was published in countries such as the USA, Singapore, Australia, and Ireland. Also, previous research supports the view that studies on blended learning in skills-based subjects are very limited and somewhat disconnected ( 27 , 31 , 43 ).

For the participants, the majority of blended learning journal articles in physical education have focused on undergraduates. This is in line with the study by Yang et al. ( 11 ) which found that researchers were more concerned with mobile learning in higher physical education. However, only a limited number of articles investigated K−12 students and teachers separately. This review discovers that blended learning can be a challenge for K−12 students as they have poor self-control and are unfamiliar with the operation of online learning platforms, making it difficult for them to watch instructional videos independently before class. As a result, some articles report several suggestions for applying blended learning in the K-12 educational setting, including determining the duration of online learning based on student attention spans ( 44 ), designing simple and streamlined online courses to create organized learning environments that enable students to improve user experience and reduce cognitive load ( 45 ), connecting online learning content to student experiences ( 46 ), creating study groups in which the teacher sets a theme and the students participate in the learning in a group form to develop the awareness of active participation and the ability to collaborate ( 47 ), providing personalized support ( 48 ), and learning through games to develop skills and knowledge related to course objectives ( 49 ). One prominent suggestion by the reviewed articles is that applying blended learning allows for the facilitation of various types of interactions ( 50 ). Among them, student-student interaction refers to peer support and collaborative learning, student-teacher interaction consists of evaluation, motivation, guidance, and prompt feedback ( 51 ), student–online learning content interaction is the process of intellectual interaction with learning content, to promote students' learning ( 52 ), and student-interface interaction refers to the interaction between students and the technology used to deliver educational content ( 53 ).

In addition, there is a limited number of articles on blended learning in physical education focusing on teachers. This may be because the selection of teachers as subjects for the study is challenging for several reasons. For example, the sample size may be too small for quantitative analysis and some teachers may be reluctant to embrace new teaching models. Nevertheless, technologies, through blended learning, offer many new opportunities for teaching. Besides that the use of blended learning could improve teachers' attitudes toward the application of technology, and it could also enhance their ability to apply technology to physical education, which is crucial for their professional development ( 54 ). Therefore, future blended learning papers in physical education should place greater emphasis on the teacher community.

Blended learning as an innovative pedagogical model requires the application of emerging methods in practice to meet specific pedagogical requirements ( 55 ). This review observed that teachers use different teaching platforms and online learning resources when incorporating blended learning in physical education in order to meet their pedagogical goals. The frequency of “learning platform” ranked highest among the selected studies, followed by “online learning materials” and “learning software.” With the development of educational technology, many student-centered learning platforms (e.g., Moodle, Superstar) are adopted by teachers in different educational institutions. These learning platforms are supported by teachers because they are powerful, easy to use, and can meet the common needs of both teachers and students ( 56 ). In addition, online learning materials which include online lectures, online documents, and online websites have also become teachers' choices. Compared to online learning platforms, online learning materials are richer in content and more diverse in learning formats. Teachers can select appropriate materials according to their student learning interests and practical needs ( 57 ). Self-developed learning tools or learning materials appropriate for the delivery of the courses are also created by teachers. One article developed and applied a robot ( 35 ), one article used recorded lectures ( 37 ), and a total of three articles used instructional software (e.g., basketball teaching mobile application) as the primary learning tool for learning activities ( 28 , 32 , 36 ). In general, while research on blended learning in physical education prior to 2020 on learning tools was homogeneous, the form diversifies as teachers begin combining two learning tools to produce better learning outcomes beginning in 2020, with the increased number of blended learning studies in physical education.

The theoretical framework is an essential component of disciplinary inquiry as it provides researchers with a strong argument for the significance of a particular research question and guides the analysis and interpretation of the data collected ( 58 ). The variety of theoretical frameworks found in reviewed articles indicates that blended learning in physical education is still in the stage of theoretical exploration, especially with twelve articles failing to specify a theoretical framework or a theoretical model used in the studies. The most commonly cited theories in this study are the self-determination theory (SDT) ( 22 , 31 ) and the constructivist theory ( 34 , 37 ). The self-determination theory asserts that individual development and progress are achieved through the satisfaction of three basic psychological needs: autonomy (self-identity and autonomy of choice), relatedness (being loved and interacting), and competence (being perceived as effective and capable). Meeting these three needs in a learning task will significantly enhance students' intrinsic motivation ( 14 ). This is because, in blended learning, students can determine their own learning time and pace based on their preferences (autonomy) and individual learning levels (competence). Blended learning also allows for collaborative learning that provides a highly interactive learning environment that meets student needs for relevance (relatedness). In short, many studies support the existing literature that blended learning environments have a positive impact on students' cognitive learning outcomes and “needs” for competence, autonomy, and relatedness ( 59 , 60 ).

On the other hand, constructivism, upholding the constructivist theory, believes that students do not passively acquire knowledge, but actively construct new understanding and knowledge through personal experience and social discourse and combine new information with existing knowledge ( 61 ). Blended learning emerged to overcome the disadvantages of passive learning in traditional physical education learning models and enhance students' learning experiences and build problem-solving skills for further practice by optimizing the combination of various learning modes. Applying constructivist theory to a blended learning environment, therefore, increases student interaction, learning efficiency, and quality ( 62 ). Post-humanist theory seeks to provide a new epistemology that is non-anthropocentric and rejects dualism as a central ( 63 ). Guided by this theory, researchers have a better understanding of the significance of online and face-to-face instruction in blended learning. Also, according to post-humanist theory, when introducing blended learning in physical education, teachers need to design and use an integrated approach so that all instructional elements, as well as their components (e.g., online instructional materials and face-to-face activities), are interacting, thus enhancing the learning experience of students ( 37 ). This review also discovers another theory associated with metacognition that stresses helping students master and reflects on their current learning situations in blended learning in physical education so that they can improve their skill performance. It is cognitive apprenticeship. Cognitive apprenticeship is an instructional model proposed by American cognitive psychologists Collins, Brown, and Duguid in 1989 that emphasizes the importance of the process by which teachers transfer skills to students. The reflective practice focuses on students' reflection on their performance in an ongoing practice for personal development.

In traditional physical education learning models, students can only passively accept knowledge and skills in the classroom. To extend the learning time and space, a new approach involving virtual learning environments has been proposed, which is the Collaborative Cyber Community (3C) model ( 64 ). This model highlights the importance of interaction and collaboration in a virtual environment where students can gain motor skills and knowledge and teachers can develop the competencies to guide students in technology-related instruction. In addition, some theoretical frameworks based on the flipped learning model were also included in some of the reviewed articles, such as the watch, summary, and question (WSQ) flipped learning model, the annotation, reflection, questioning, and interflow (ARQI) flipped learning model, and the identification, communication, reflection, and analysis (ICRA) flipped learning model. The watch, summary, and question (WSQ) flipped learning model aims to guide students to mark key points and difficulties when watching instructional videos and summarize and ask questions during the before-class stage to promote students' understanding of the learning content ( 29 ). Even though students can focus on understanding the learning content through WSQ flipped learning model, there is a lack of practical experience and reflection on motor skills. In contrast, practice videos in the annotation, reflection, questioning, and interflow (ARQI) flipped learning model facilitate students' ability to observe their sports performance from a spectator's perspective and critically reflect on their motor skills and internal experiences, thus allowing them to improve their performance ( 30 ). Similarly, based on the educational theory of reflective practice, the Identification, Communication, Reflection, and Analysis (ICRA) flipped learning model was developed to improve the effectiveness of flipped sports learning and to create pedagogies that are more suitable for motor skill learning ( 39 ).

Evaluation for learning is a method used for instruction that provides feedback to students and teachers to promote learning and guide the next stage of action. Feedback includes informal feedback (e.g., immediate verbal comments on student performance or behavior) and formal feedback (e.g., written feedback given at the end of a test and recorded as evidence for use by the student and the organization). Evaluation to facilitate learning involves high-quality peer assessment of learning with each other and self-assessment, with the results used as a basis for deciding what will be learned in the future ( 65 ). In terms of evaluation methods, this review found half of the articles used formal feedback (tests), with questionnaires and interviews being the most common of the other feedback methods. Other evaluation methods such as lesson observation, field notes, document analysis ( 34 ), and reflective blogging ( 37 ) were also mentioned, indicating the diversity of assessment methods of blended learning in physical education research. In addition, it is worth noting that five articles in this review used two evaluation methods, while six articles used three or more evaluation methods. This is in line with the current research trend where mixed methods research is increasingly valued in social science research as it provides a better understanding of what blended learning entails and how it can support student learning in a variety of ways ( 66 ).

In terms of the application areas of blended learning in physical education, the dynamic domain was explored the most, indicating that at this stage, the research on blended learning in physical education is mainly focused on physical exercise, which is in line with the characteristics of physical education. Even though studies have been investigating blended learning in single sports, such as dance, basketball, football, and Wushu, the sports categories are limited and lack richness. Moreover, this review discovers that the physical education theory (PET) curriculum is currently a less studied ( 37 , 42 ), probably because it is mainly conducted in higher education. However, it still has a vital role to play in the development of physical education. These two articles on the physical education theory (PET) curriculum only used interviews and questionnaires to investigate teachers' and students' experiences and satisfaction, so future research could use other research methods such as experimental and mixed methods to further investigate students' effectiveness and depth of perception. Furthermore, three articles explored both theoretical and pedagogical activity aspects of the physical education curriculum, such as the Physical Activity and Wellness ( 22 ), Sports Coaching ( 23 ), and Sports Management ( 40 ). The findings showed that there are different specificities to the use of blended learning, particularly the collaborative nature between students, experiential learning, the increased autonomy of students in their learning process, and the greater effect of critical thinking. Students receive more guidance and feedback from teachers in classroom activities, which is impossible to achieve with traditional teaching methods.

The findings from the dimension of the Research Topic reveal that perceptions ( n = 13), as well as learning effects ( n = 12) and satisfaction ( n = 6), have been the main concerns of researchers when conducting blended learning studies, in addition to motivation ( n = 4) and self-efficacy ( n = 4). This is largely in line with the study by Chen et al. ( 67 ) which flipped the science classroom and found that the researchers were more concerned with the student's learning effects, as well as their perceptions and attitudes/motivation. This is justified because blended learning is a new approach for most teachers and hence, it is essential to examine the impact of a relatively new pedagogical model on students' academic performance and perceptions. However, from the review of 22 articles, blended learning in physical education has generally met researchers' expectations. For instance, several studies mentioned the positive impacts of blended learning on students' learning effects, self-efficiency, interaction, and satisfaction ( 23 , 27 , 28 , 32 , 35 ), as well as their perceptions, motivation, and attitude ( 31 , 36 , 38 , 41 , 42 ). Furthermore, other topics such as the task load ( 29 , 30 ), attendance ( 24 ), self-assessment tools ( 25 ), skills and career development ( 26 ), and psychological needs in sports ( 33 ) were also conducted. The findings show that blended learning in the field of physical education, though in a developmental stage, meets the expectations of researchers.

While the advantages of blended learning models in optimizing teaching and learning are evident in countless influential studies, incorporating technology into education also brings a degree of unease to students and teachers. The most common problem related to blended learning in physical education is the instructional design challenge. Researchers have recently begun to develop or use online technologies for teaching or training activities. However, due to its specificity and complexity, physical education is more difficult to design in blended learning than other academic learning activities ( 68 ). The research by Boelens et al. ( 69 ) identifies four key challenges in the design of blended learning environments: incorporating flexibility, facilitating interaction, facilitating the learning process for students, and creating an effective learning environment. The shortcomings of instructional designs such as a lack of variety in content ( 29 , 34 ) and lengthy videos ( 23 ) are mentioned in several articles. Also, Liu et al. ( 42 ) report that students experience a sense of distance when involved in too many online learning activities. Tsai et al. ( 70 ) concur stating that online courses in blended learning should only be offered every 2 weeks so that students can learn on their own and, if they encounter problems, they can solve them through face-to-face interaction. Another challenge is the technological literacy and competency that have become necessary for teachers and students to pursue contemporary education. The findings of López-Fernández et al. ( 17 ), Lucena et al. ( 32 ), and Reddan et al. ( 23 ) emphasize the lack of literacy and competency among students and teachers in using technology. Liu et al. ( 42 ) mention that students are more conservative in enhancing their information-related skills, which affects their learning outcomes and satisfaction with the course. Similarly, Hsia et al. ( 29 ) highlight the need for blended-learning students to be technologically competent because incompetence with learning technology can be a barrier to students' success in blended learning.

Another challenge for students in blended learning is that they are expected to self-regulate their learning activities outside of face-to-face classes. Two articles specifically identified the types of self-regulation challenges, namely procrastination ( 42 ) and improper time management ( 29 ). It is worth noting that procrastination is considered a chronic habit of unnecessarily putting off things that need to be done ( 71 ). Students' procrastination behavior differs in traditional and blended models, as students in blended learning environments experience a more pronounced sense of transactional distance ( 8 ). Belief challenges in this study refer to the negative attitudes and perceptions of teachers or students regarding the use of technology for teaching and learning. As reported by Brown ( 72 ), the difficulties encountered in adopting technology may be seen as disruptive to teaching and learning. Teachers may think of blended learning as instruction that has two teaching sections to deal with. For example, some physical education teachers believe that blended learning meant extra work compared with traditional teaching ( 17 ). Chao et al. ( 38 ) also report that students are reluctant to accept pre-class preparation. Furthermore, past research has mentioned that student learning activities, such as homework and preparation before face-to-face lectures, are challenging due to the alienation and loneliness felt by students online. Similarly, the study by López-Fernández et al. ( 17 ) finds that alienation and loneliness were also a challenge for physical education teachers because they find it more challenging to establish social relationships, either between teachers and students or between students, in the blended learning model than in the traditional model. This view was confirmed by a previous study of blended learning in physical education, where teachers felt disconnected from students and expressed concerns associated with the potential lack of social relationships and learning opportunities for students in a virtual environment ( 73 ).

4.2. Limitation

First, this study is limited by the use of rich eligibility criteria and methodology to consider only high-impact journals. Referring to other databases such as Google Scholar, PubMed, or Scopus might have resulted slightly differently. Second, only articles written in English are chosen. Third, the definition of blended learning opted in this review is a combination of traditional and online learning, so articles that do not conform to this definition are excluded, such as those that only mention the face-to-face part of blended learning. Finally, the study only focuses on the application of blended learning in physical education, such as the development trends and the main findings of current research. Therefore, the results cannot be extended to all research dimensions. Nevertheless, this research should be adequate to provide a roadmap for future research on blended learning in physical education.

5. Conclusion and suggestions

According to the overall findings, blended learning is in the initial stages of its development in the field of physical education. This result can be seen in several ways. First, researchers around the world have tried to apply blended learning in physical education, but the number of high-quality studies is very limited. Second, the majority of participants in the studies of blended learning on physical education are undergraduates, and a limited number of studies have been conducted on other subjects such as K−12 students and teachers. This review also reveals that studies prefer to investigate proven learning tools and the materials chosen by teachers as pre-course learning materials based on their personal preferences. In terms of theoretical framework, half of the researchers in the field of blended learning in physical education tend to not mention any theoretical framework. In addition, many prefer adopting a single evaluation method, with questionnaires being the most common method. Moreover, the focus of most journal articles on blended learning in physical education are on the preliminary aspects of blended learning research, namely perceptions, learning outcomes, satisfaction, and motivation. This leaves room for further research. This review also discovers that the most studied item in most articles on blended learning in physical education is dance. However, the majority of studies take a broad approach by not mentioning any specific item of physical education. Finally, the most common challenges for students and teachers revealed in this review are instructional design challenges, technological literacy and competency challenges, self-regulation challenges, alienation and isolation challenges, and belief challenges. In conclusion, this review provides a foundation for the future development of blended learning models by demonstrating the current status and development trends of blended learning in physical education.

Based on the results and discussion of the current review, several recommendations regarding blended learning in physical education are presented. First, it is necessary to improve the skills and perceptions of teachers. It is also evident that the researchers are very concerned about student perceptions of blended learning and learning outcomes. Most teachers and students identify instructional design and technological literacy and competence as their most obvious challenges. This implies that teachers need more training to improve their course design and management of online classes, including the use of multiple technologies as instructional support tools and the design of learning activities with various strategies at different stages of blended learning. To further explore the impact of blended learning on physical education, future research needs to focus on other populations (K−12 students, teachers, and educational institutions) and situations in other countries or regions. Future research should also focus on the application of blended learning in static physical education. Furthermore, it is recommended that the potential of blended learning in other sports be explored. In terms of the Research Topic, apart from the perceptions and learning effects, other aspects such as psychological needs and influencing factors should also be investigated.

Data availability statement

Author contributions.

Conceptualization, software, formal analysis, investigation, resources, and writing—original draft preparation: CW. Methodology: CW and YY. Validation, writing—review and editing, visualization, and project administration: CW and RO. Data curation: CW and XJ. Supervision: RO, KS, and NM. All authors have read and agreed to the published version of the manuscript.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Data-Driven Decision Model for Machine Learning Model Selection

Description.

Context: Machine learning methods are easily accessible and widely used, given how straightforward they are to employ on a predictive modeling dataset. While the performance of these libraries is stable, the challenge exists for a data scientist to choose the correct combinations of models for their machine learning domain related task. Besides sufficient performance, we consider many machine learning model related features. Method: To address this challenge, we present a meta-model for the decision-making process in the context of machine learning model selection. the creation of this decision model adopts a systematic research approach, combining systematic literature review , expert interviews, case studies, and design science to investigate machine learning model selection approaches. Where the systematic literature review enables us to gather and analyze relevant information from existing literature. The expert interviews allow a critical approach to our collected data. the case studies help us to assess the practical applicability of our findings. And the design science allows for the finalization of a decision model. Results: Our study analyzed 43 common models across 72 common features. We provide a comprehensive machine learning taxonomy, featuring machine learning paradigms, approaches, and domains. We provide insights into potential model combinations, trends in model selection, quality concerns, evaluation measures, and frequently used datasets for training and evaluating these models. Contribution: Our study shows the effectiveness and robustness of our decision model, contributing practical insight and comprehensive understanding to the field of machine learning model selection. We highlight the importance of further development of the decision model, to improve its accuracy and scope beyond its current state.

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