problem solving skill development through stem learning approaches

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• DOI: 10.1109/FIE49875.2021.9637226
• Corpus ID: 245388761

Problem-solving skill development through STEM learning approaches

• Khaula Zeeshan , C. Watanabe , P. Neittaanmäki
• Published in Frontiers in Education… 13 October 2021
• Education, Engineering, Computer Science

3 Citations

An integrative learning model to improve problem-solving and creative thinking abilities, collaboration, and motivation, bridging the gender gap through problem-based learning in stem labs: what can we learn from biotechnology, chatgpt for stem education: a working framework, related papers.

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• Problem Solving in STEM

Solving problems is a key component of many science, math, and engineering classes.  If a goal of a class is for students to emerge with the ability to solve new kinds of problems or to use new problem-solving techniques, then students need numerous opportunities to develop the skills necessary to approach and answer different types of problems.  Problem solving during section or class allows students to develop their confidence in these skills under your guidance, better preparing them to succeed on their homework and exams. This page offers advice about strategies for facilitating problem solving during class.

How do I decide which problems to cover in section or class?

In-class problem solving should reinforce the major concepts from the class and provide the opportunity for theoretical concepts to become more concrete. If students have a problem set for homework, then in-class problem solving should prepare students for the types of problems that they will see on their homework. You may wish to include some simpler problems both in the interest of time and to help students gain confidence, but it is ideal if the complexity of at least some of the in-class problems mirrors the level of difficulty of the homework. You may also want to ask your students ahead of time which skills or concepts they find confusing, and include some problems that are directly targeted to their concerns.

You have given your students a problem to solve in class. What are some strategies to work through it?

• Try to give your students a chance to grapple with the problems as much as possible.  Offering them the chance to do the problem themselves allows them to learn from their mistakes in the presence of your expertise as their teacher. (If time is limited, they may not be able to get all the way through multi-step problems, in which case it can help to prioritize giving them a chance to tackle the most challenging steps.)
• When you do want to teach by solving the problem yourself at the board, talk through the logic of how you choose to apply certain approaches to solve certain problems.  This way you can externalize the type of thinking you hope your students internalize when they solve similar problems themselves.
• Start by setting up the problem on the board (e.g you might write down key variables and equations; draw a figure illustrating the question).  Ask students to start solving the problem, either independently or in small groups.  As they are working on the problem, walk around to hear what they are saying and see what they are writing down. If several students seem stuck, it might be a good to collect the whole class again to clarify any confusion.  After students have made progress, bring the everyone back together and have students guide you as to what to write on the board.
• It can help to first ask students to work on the problem by themselves for a minute, and then get into small groups to work on the problem collaboratively.
• If you have ample board space, have students work in small groups at the board while solving the problem.  That way you can monitor their progress by standing back and watching what they put up on the board.
• If you have several problems you would like to have the students practice, but not enough time for everyone to do all of them, you can assign different groups of students to work on different – but related - problems.

When do you want students to work in groups to solve problems?

• Don’t ask students to work in groups for straightforward problems that most students could solve independently in a short amount of time.
• Do have students work in groups for thought-provoking problems, where students will benefit from meaningful collaboration.
• Even in cases where you plan to have students work in groups, it can be useful to give students some time to work on their own before collaborating with others.  This ensures that every student engages with the problem and is ready to contribute to a discussion.

What are some benefits of having students work in groups?

• Students bring different strengths, different knowledge, and different ideas for how to solve a problem; collaboration can help students work through problems that are more challenging than they might be able to tackle on their own.
• In working in a group, students might consider multiple ways to approach a problem, thus enriching their repertoire of strategies.
• Students who think they understand the material will gain a deeper understanding by explaining concepts to their peers.

What are some strategies for helping students to form groups?  

• Instruct students to work with the person (or people) sitting next to them.
• Count off.  (e.g. 1, 2, 3, 4; all the 1’s find each other and form a group, etc)
• Hand out playing cards; students need to find the person with the same number card. (There are many variants to this.  For example, you can print pictures of images that go together [rain and umbrella]; each person gets a card and needs to find their partner[s].)
• Based on what you know about the students, assign groups in advance. List the groups on the board.
• Note: Always have students take the time to introduce themselves to each other in a new group.

What should you do while your students are working on problems?

• Walk around and talk to students. Observing their work gives you a sense of what people understand and what they are struggling with. Answer students’ questions, and ask them questions that lead in a productive direction if they are stuck.
• If you discover that many people have the same question—or that someone has a misunderstanding that others might have—you might stop everyone and discuss a key idea with the entire class.

After students work on a problem during class, what are strategies to have them share their answers and their thinking?

• Ask for volunteers to share answers. Depending on the nature of the problem, student might provide answers verbally or by writing on the board. As a variant, for questions where a variety of answers are relevant, ask for at least three volunteers before anyone shares their ideas.
• Use online polling software for students to respond to a multiple-choice question anonymously.
• If students are working in groups, assign reporters ahead of time. For example, the person with the next birthday could be responsible for sharing their group’s work with the class.
• Cold call. To reduce student anxiety about cold calling, it can help to identify students who seem to have the correct answer as you were walking around the class and checking in on their progress solving the assigned problem. You may even want to warn the student ahead of time: "This is a great answer! Do you mind if I call on you when we come back together as a class?"
• Have students write an answer on a notecard that they turn in to you.  If your goal is to understand whether students in general solved a problem correctly, the notecards could be submitted anonymously; if you wish to assess individual students’ work, you would want to ask students to put their names on their notecard.  
• Use a jigsaw strategy, where you rearrange groups such that each new group is comprised of people who came from different initial groups and had solved different problems.  Students now are responsible for teaching the other students in their new group how to solve their problem.
• Have a representative from each group explain their problem to the class.
• Have a representative from each group draw or write the answer on the board.

What happens if a student gives a wrong answer?

• Ask for their reasoning so that you can understand where they went wrong.
• Ask if anyone else has other ideas. You can also ask this sometimes when an answer is right.
• Cultivate an environment where it’s okay to be wrong. Emphasize that you are all learning together, and that you learn through making mistakes.
• Do make sure that you clarify what the correct answer is before moving on.
• Once the correct answer is given, go through some answer-checking techniques that can distinguish between correct and incorrect answers. This can help prepare students to verify their future work.

How can you make your classroom inclusive?

• The goal is that everyone is thinking, talking, and sharing their ideas, and that everyone feels valued and respected. Use a variety of teaching strategies (independent work and group work; allow students to talk to each other before they talk to the class). Create an environment where it is normal to struggle and make mistakes.
• See Kimberly Tanner’s article on strategies to promoste student engagement and cultivate classroom equity. 

A few final notes…

• Make sure that you have worked all of the problems and also thought about alternative approaches to solving them.
• Board work matters. You should have a plan beforehand of what you will write on the board, where, when, what needs to be added, and what can be erased when. If students are going to write their answers on the board, you need to also have a plan for making sure that everyone gets to the correct answer. Students will copy what is on the board and use it as their notes for later study, so correct and logical information must be written there.

For more information...

Tipsheet: Problem Solving in STEM Sections

Tanner, K. D. (2013). Structure matters: twenty-one teaching strategies to promote student engagement and cultivate classroom equity . CBE-Life Sciences Education, 12(3), 322-331.

• Designing Your Course
• A Teaching Timeline: From Pre-Term Planning to the Final Exam
• The First Day of Class
• Group Agreements
• Classroom Debate
• Flipped Classrooms
• Leading Discussions
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• Teaching with Cases
• Engaged Scholarship
• Devices in the Classroom
• Beyond the Classroom
• On Professionalism
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• Published: 19 December 2019

Direction of collaborative problem solving-based STEM learning by learning analytics approach

• Li Chen   ORCID: orcid.org/0000-0003-0063-8744 1 ,
• Nobuyuki Yoshimatsu 2 ,
• Yoshiko Goda 3 ,
• Fumiya Okubo 4 ,
• Yuta Taniguchi 5 ,
• Misato Oi 6 ,
• Shin’ichi Konomi 7 ,
• Atsushi Shimada 3 ,
• Hiroaki Ogata 8 &
• Masanori Yamada 7  

Research and Practice in Technology Enhanced Learning volume  14 , Article number:  24 ( 2019 ) Cite this article

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The purpose of this study was to explore the factors that might affect learning performance and collaborative problem solving (CPS) awareness in science, technology, engineering, and mathematics (STEM) education. We collected and analyzed data on important factors in STEM education, including learning strategy and learning behaviors, and examined their interrelationships with learning performance and CPS awareness, respectively. Multiple data sources, including learning tests, questionnaire feedback, and learning logs, were collected and examined following a learning analytics approach. Significant positive correlations were found for the learning behavior of using markers with learning performance and CPS awareness in group discussion, while significant negative correlations were found for some factors of STEM learning strategy and learning behaviors in pre-learning with some factors of CPS awareness. The results imply the importance of an efficient approach to using learning strategies and functional tools in STEM education.

Introduction

In the twenty-first century, international concern over science, technology, engineering, and mathematics (STEM) education has increased. We currently face many global issues, including climate change, overpopulation and wellbeing, resource management, health, and biodiversity, that put great pressure on institutions involved in developing science and technology and that require continued development of STEM education (Gough, 2015 ; Thomas & Watters, 2015 ). Considering the complexity and diversity of these issues and the great need for the ability to integrate knowledge and skills in science, technology, engineering, and mathematics to solve real problems (Newhouse, 2016 ), science learning seems to be a powerful way of thinking and understanding the basis of these issues (Thomas & Watters, 2015 ).

According to Kelley and Knowles ( 2016 ), it is indicated that the foundation of STEM learning framework is the situated STEM learning, which is based on the situated cognition theory. Situated cognition theory emphasizes that learners’ knowledge is constructed within the authentic activities and contexts (Brown, Collins, & Duguid, 1989 ; Lave & Wenger, 1991 ), and not only knowledge and skill itself, how knowledge and skill can be applied into the authentic contexts is also important (Kelley & Knowles, 2016 ). Therefore, as one of the critical themes of STEM, acquiring knowledge and skills through solving problems with real-world scenarios is crucial in STEM learning, for the reasons such as it helps students to prepare for real life, and solve life problems and work problems, by using science, technology, engineering, and mathematics-related knowledge and skills (Holmlund, Lesseig, & Slavit, 2018 ).

Moreover, situated cognition theory recognizes that not only the cognitive aspect but also the social aspect of learning activities are critical to the learning process. It is emphasized the opinion that, rather than constructing knowledge on one’s own, people’s knowledge is constructed through socially communicating and interchanging with others (Lave & Wenger, 1991 ). In situated STEM learning, it is considered that knowledge is organized around ideas, concepts, or themes, and evolved through social discourse, thus, as one of the key elements of situated learning, a community of practice is considered as the rope of all dimensions of STEM, which connects science inquiry, technological literacy, mathematical thinking, and engineering design (Kelley & Knowles, 2016 ). It is indicated that not only acquiring the knowledge and skills itself, but also the process of how to acquire them through the authentic contexts and the exchange of ideas, and how to use them to solve authentic problems, which including both cognitive and social aspects, should be considered in STEM education.

Considering the integrative nature of STEM education, it has been indicated that collaboration for problem solving provides some benefits, such as a more effective division of labor and the incorporation of differing perspectives, knowledge, and experiences (Organisation for Economic Cooperation and Development [OECD], 2017 ). In this regard, collaborative problem solving (CPS) is considered effective in STEM education, especially when dealing with real and complex problems, because of its coordinated nature (Hesse, Care, Buder, Sassenberg, & Griffin, 2015 ).

According to Hesse et al. ( 2015 ), CPS is not a uniform but rather a complex and coordinated skill that comprises several sub-factors. In order to develop CPS skills in STEM education and improve learning effectiveness, it is important to identify the potential influential factors for learning performance and CPS awareness in STEM learning.

In many prior studies, learning behaviors have proved to be important indicators of students’ learning performance (Hwang, Shadiev, Wang, & Huang, 2012 ; Lowes, Lin, & Kinghorn, 2015 ) and individual learning awareness of instructional practice (Artino Jr. & Jones II, 2012 ; Yamada et al., 2017 ). Since STEM learning allows students to combine theory and practice in real situations, it is necessary to explore the aspects of cognition and behaviors in STEM, including how students comprehend and apply integrated knowledge (Liu & Cavanaugh, 2011 ). Thus, we took learning behaviors into consideration as important factors that could affect the improvement of learning performance and cultivation of CPS awareness.

Since it is not easy to capture students’ learning behaviors during lessons, instructional experiments that focus on learning behaviors have mostly targeted online courses in higher education or long-term courses in secondary education. However, not all instructors in secondary education will adopt online instructions in the classes due to time constraints, limitations of equipment, and other issues. Therefore, we try to examine students’ learning behaviors that reflect their individual thinking and investigate their influence on learning performance and CPS skills utilization.

In addition to learning behaviors, cognitive learning strategy is also considered a factor affecting learning scores (Yamada et al., 2016 ). Since STEM education is an integrated learning approach (Kelley & Knowles, 2016 ; Lou, Liu, Shih, & Tseng, 2011 ), it is important for both educators and students to understand the coherence, integration, and learning approaches of STEM so that they can apply an effective learning strategy in STEM teaching and learning. As indicators of learning behaviors, it is necessary to make sense of students’ cognitive learning strategies in STEM education, such as whether and how they use their learning strategies during STEM learning and when solving STEM problems (Griese, Lehmann, & Roesken-Winter, 2015 ).

Based on insights from these prior studies and the needs they revealed for understanding the factors that might affect learning performance and learning awareness, our study aimed to clarify the correlations of students’ learning performance and learning awareness with the variables of their learning strategy and learning behaviors to determine the factors that might affect STEM CPS learning and provide an effective way for researchers and instructors to develop and instructional methods in STEM CPS education.

Literature review

Collaborative problem solving in stem education.

STEM includes scientific study, technology, engineering design, and mathematical analysis (Lou et al., 2011 ). STEM teaching is commonly conceptualized as a multidisciplinary approach that typically begins with a discipline or multidiscipline, on the basis of which instructors prepare problems for students to solve (Herro, Quigley, Andrews, & Delacruz, 2017 ). As one of the successful factors in STEM education, it is important not only to embed knowledge and skills in the curriculum but also to assess knowledge and skills in a real situation or problem-solving practice process and focus on the link of knowledge between the four STEM domains (Newhouse, 2016 ). STEM teaching allows students to examine and apply theories and knowledge to improve their problem-solving skills, as well as to integrate the comprehension and application of complicated knowledge in STEM areas (Lou et al., 2011 ).

Although it is not suggested that all four STEM domains must be embedded in one STEM curriculum or learning experience, it is still necessary to understand the relationships among these domains and seek coherency in STEM education (Kelley & Knowles, 2016 ). Thus, Kelley and Knowles emphasized the importance of STEM integration and defined integrated STEM education as “the approach to teaching the STEM content of two or more STEM domains, bound by STEM practices within an authentic context for the purpose of connecting these subjects to enhance student learning” (p. 3). Based on this definition, we emphasize the solution of authentic science problems across the STEM domains in order to enhance learners’ integrated STEM-related knowledge and skills. To achieve that goal, the application of scientific concepts, the individual’s interaction with technology, applied mathematics, and engineering design (Kelley & Knowles, 2016 ; Lou et al., 2011 ) are considered the important factors of STEM instruction design in our study.

In STEM education, situated learning is considered as the foundation of the integration of four domains (Kelley & Knowles, 2016 ), in which students can build an increasing rich understanding of what they are learning, through applying knowledge and skills actively to the authentic situation, rather than just acquire them (Brown et al., 1989 ). Authentic activities is helpful for students to act meaningfully and purposefully, since by conducting authentic activities, students are provided with experience to represent and describe the knowledge or concepts, and revise their understanding and actions based on the experience and results (Brown et al., 1989 ). Based on the situated STEM learning, an engineering design approach provides the opportunity for students to build connections among STEM domains and apply science knowledge and inquiry in an authentic context. Students can construct new knowledge and enhance their learning through engineering practice and scientific inquiry (Kelley & Knowles, 2016 ). Students treat technology and engineering as cognitive tools, apply mathematical and scientific approaches to solve problems, generalize key concepts, and accumulate procedural knowledge (Lou et al., 2011 ). Thus, students are expected to develop and use their integrated knowledge and cognitive skills such as problem-solving skill, through authentic contexts. Additionally, in light of the importance of the social aspect of STEM learning, Kelley and Knowles ( 2016 ) point out that a community of practice (Lave & Wenger, 1991 ) is an important element in integrating the four STEM domains in situated learning, since students can construct their understanding by expressing and interpreting their thinking, and rich understanding by the exchange of the ideas and the communication and negotiation with others (Brown et al., 1989 ). Therefore, it is important to take not only social aspect into consideration in STEM learning.

According to the OECD ( 2017 ), collaboration for problem solving affords potential advantages over individual problem solving, including the fact that collaboration affords more effective division of labor; incorporation of information from group members with multiple perspectives, experiences, and sources of knowledge; and enhanced creativity and quality of solutions through mutual feedback. Therefore, in order to improve integrated STEM learning, it is necessary to consider social and cognitive factors when tackling STEM challenges. In this regard, collaborative problem solving (CPS) is a promising area in STEM education because of its advantages in inculcating the understanding of scientific knowledge and others’ ideas, training in scientific investigation, and solving applied problems (Hesse et al., 2015 ; Hogan, 1999 ; Lin et al., 2015 ). Lin, Yu, Hsiao, Chang, and Chien ( 2018 ) compared the effectiveness of web-based CPS system and the classroom-based hands-on activities including the CPS scenarios from daily life, in order to develop students’ CPS skills in STEM learning. As their results, the virtual STEM learning environment was found to be more effective in the development of their CPS skills than traditional classroom-based hands-on activities, and the effectiveness of the system would be further enhanced with teacher’ involvement and guidance (Lin et al., 2018 ).

According to Hesse et al. ( 2015 ), CPS combines two domains, the social domain and the cognitive domain. The social domain, which refers to collaboration, focuses on managing the interaction and contributions of individuals, while the cognitive domain, which means problem solving, requires effort in task regulation and the application of skills (Care, Scoular, & Griffin, 2016 ). Due to the complex and coordinated nature of CPS, it is important to identity the factors that might influence all the sub-skills of CPS and how all sub-skills develop. Our previous study indicated that students’ behavioral factors affect learning performance and CPS awareness (Chen et al., 2018 ). Therefore, in the present study, in order to improve the learning effectiveness of STEM CPS lessons, we explored the relationships of learning effectiveness with learning behaviors and cognitive learning strategies to see how individual cognition and thinking affect CPS learning in order to determine the factors that might impact the improvement of knowledge and CPS awareness.

Learning analytics and online behaviors

Considering the social and cognitive domains of CPS, it is important to clarify how students engage in collaboration activities and how they act and think in individual and collaborative learning (Chen, L et al., 2018 ). In this regard, it is important to understand students’ individual thinking behaviors and how to use learning strategies in individual and group learning. In light of the difficulty of collecting behavioral data during traditional face-to-face lectures, many studies have focused on educational data collected and analyzed technologically using a learning analytics approach.

According to Dunbar, Dingel, and Prat-Resina ( 2014 ), it is important to incorporate the educational data and analysis relevant to student learning into course and curriculum design, and they also indicated a need for methods and tools that curriculum designers can use to explore data on instructional practices. In science education, the analytics and mining of educational data are useful in evaluating and improving educational design (Monroy, Rangel, & Whitaker, 2014 ). During the past decades, researches put efforts to explore the potential of analytics and data mining techniques and methodologies, to extract valuable and actionable information from large datasets. When applied to education, these methodologies are referred to as learning analytics (LA) and educational data mining (EDM).

Although there are many similarities between LA and EDM, in general methods and procedures, including gathering, processing, reporting, and acting on machine-readable data in order to improve the educational environment, LA and EDM differ in many aspects, such as origins, types of discovery, and adaption and personalization (Baker & Siemens, 2014 ; Liñán & Pérez, 2015 ). For example, origins of researches in EDM are related to educational software and student modeling, and are more interested in automated discovery, and look for the automated adaptation for supporting learners, such as identifying learner’s need and automatically change to personalize learner’s experience with the help of educational software. Whereas, LA researches are rooted in curriculum, outcome prediction, and systemic interventions, and are interested in human-led methods to explore the data for more interpretable and understandable models. Rather than automated adaptation, researches of LA look for ways to inform and empower instructors and learners, such as informing instructors or learners themselves concerning specific learners’ learning situation and processes.

The present study aims at exploring the relationships between learning performance and CPS awareness with behavioral and STEM strategical factors. In order to achieve the research aims, the experiment was conducted based on the instructional design, with the interventions from the designers and the instructor. In this research, educational data including not only learning logs which were collected and generated by the system but also the psychological data conducted by the survey. The methods of data collection and analysis were based on human-led methods according to the learning theories and the changing situations of the learning environment.

The purpose of the educational data analysis in this experiment is to clarify the relationships between various factors and predict the potential factors which may influence learning outcomes, and then inform instructors and learners these results to help them understand their teaching or learning, and providing researchers and educators some implications in CPS-based STEM learning. Therefore, we adopted LA approach, rather than EDM approach, to achieve the above research purpose.

Learning analytics (LA) is an effective way of using academic data that allows us to understand and improve learning in various fields. For example, Bazelais, Lemay, and Doleck ( 2018 ) investigated the relationship between students’ prior achievement (high school average) with performance in a pre-university physics program by using data of 9877 students’ pre-university physics course. As the results, they found that prior high school performance achievement was a strong predictor of college physics course performance. In higher education, support of the technology makes it possible to collect LA data from new resources such as learning management systems (LMS). Through LMS, students’ learning logs can be collected and data from them can be used to define learning behavior variables when conducting online learning, such as number of logins, pages accessed, and time spent in the system, which can show students’ frequency and duration of participation (Morris, Finnegan, & Wu, 2005 ). Moreover, some studies have found that certain types of online participation behaviors, such as “page hits,” are correlated with grades (Ramos & Yudko, 2008 ; Wang & Newlin, 2000 ). By using an e-book system, students can use multiple functions of e-books such as going to the next page or previous page, adding bookmarks, underlining, annotations, and keyword searching, and all these log data can be collected by the system. Additionally, the system can also know when and for which course the e-book was used, which is very useful information in analyzing students’ learning activities (Ogata et al., 2015 ). Oi, Okubo, Shimada, Yin, and Ogata ( 2015 ) analyzed students’ learning behaviors by collecting e-book logs before and after the main content learning in class to investigate the relationships of preview and review behaviors with academic achievement. Their findings indicate that previewing is more deeply relevant to academic achievement and assessment than reviewing.

Although LA is considered an effective method to assess how online behaviors are associated with learning performance in secondary education (Lowes et al., 2015 ), there are still fewer studies using LA data at the secondary education level than higher education.

Chang et al. ( 2017 ) collected and analyzed multiple data sources, including group discourse, test scores, questionnaire feedback, and problem-solving activity logs, to understand their respective learning effectiveness and CPS patterns, then examined how students solved problems using individual-based and collaborative simulations to understand their effects on science learning. However, the activities in Chang et al.’s study lasted only 60 min, which is hardly likely to be representative of other learning settings. Considering the limitations in student numbers and subjects, multi-subject studies at secondary education level should be conducted that look at large numbers of students across settings, which would add the individual subjects as a complicating factor (Lowes et al., 2015 ). Liu and Cavanaugh ( 2011 ) collected learning logs for one academic year in biology courses in high school to show that certain variables could affect student academic achievement. For example, the time students spent in the LMS positively and significantly affected their final scores in biology courses. Lowes et al. ( 2015 ) also explored LMS data for one academic year to examine the link between in-course behaviors and course outcomes, concluding that the level of online behaviors associated with attendance and interactivity showed a positive influence on final grades.

However, as it is difficult to conduct continuous online courses in secondary education due to time restrictions, limitations of equipment, and other issues, in our present study we collected students’ learning logs during short-term online learning in order to revise and improve our next short-term design accordingly.

Based on the insights of previous studies, the present study aimed at exploring the relationships of learning performance and CPS awareness with STEM learning strategy and online learning behaviors in order to investigate potential influencing factors on the learning effectiveness of STEM CPS learning and find an effective method to improve STEM CPS learning.

Research questions

As one of the goals of STEM learning, learners are expected to acquire knowledge during the activities of solving problems with authentic contexts, or apply them into authentic problems (Holmlund et al., 2018 ). In order to achieve this goal, it is important to understand how do learners acquire and construct related knowledge. The literature review cited above revealed that students’ learning behaviors engaged in individual activities of learning materials, which were related to their scientific thinking, had influence on learning performance and learning awareness. Therefore, in the present study, we try to examine the relationships between learning performance with learning behaviors on how they read and understand scientific contents, and to find out what kind of learning behaviors should be recommended or paid attention during STEM learning. Meanwhile, in order to understand how to instruct students to use effective learning strategies to acquire knowledge, it is necessary to identify what kind of STEM learning strategies would have influence on learning performance.

Moreover, since this was not the first time students got touch in the learning for CO 2 -related knowledge and the form of group work, students might have different prior knowledge and CPS awareness, which would have influence on the final results. Therefore, considering the different scores of students’ pre-test and CPS pre-questionnaire, it was not suitable to only consider the final results of learning performance and CPS awareness. Therefore, as the first research question, the difference in pre-posttests and questionnaires was examined, and to find out the relationships between the change in learning performance and CPS awareness, with learning behaviors and STEM learning strategies, to find out the potential factors that were related with the change in learning performance.

Additionally, some of the previous researches cited above showed that it is important to identify the characteristics of all the sub-factors of CPS skills when developing CPS skills, such as what kinds of relationships these sub-factors have with other learning factors. Therefore, the second research question was set to examine the relationships between the improvement in CPS awareness with behavioral and strategic factors.

Moreover, in order to understand how students would conduct these behaviors, it is necessary to associate these learning behaviors with the learning strategies they used during the STEM learning. Then students and instructors could be provided with some suggestions on the learning behaviors which had relationships with the improvement in learning performance and CPS awareness, and how to encourage students to conduct these learning behaviors by using related learning strategies.

In this study, we set three research questions:

Research question 1: Which factors of STEM learning strategy and learning behaviors have relationships with the change in learning performance in STEM learning?

Research question 2: Which factors of STEM learning strategy and learning behaviors have relationships with the change in CPS awareness in STEM learning?

Research question 3: What are the relationships between STEM learning strategy and learning behaviors in STEM learning?

Procedure of the instructional design

In this study, we designed a STEM CPS lesson based on the CPS framework proposed by Hesse et al. ( 2015 ). The theme of this STEM lesson is the same as the science lesson conducted in our prior study (Chen, Uemura, Goda, et al., 2018 ), which involved determining the reason for the Limnic Eruption , a natural disaster that occurred in Cameroon.

In order to facilitate pre-learning and group discussion, as well as to collect and analyze the learning behaviors of participants and pre-learning and individual thinking behaviors during group work, we integrated a BookRoll system into our design.

The BookRoll system is used as an e-book reader system to store lecture materials such as slides or notes (Ogata et al., 2017 ). Students can access these learning materials both in class and at home, which makes it possible to collect the learning logs from when students prepare or review their lessons to understand their learning conditions. In addition, as students can use additional functions such as highlighting, annotating, and searching for key words, these learning logs can all be collected for further analysis and instruction improvement. The interface for BookRoll used in this study is presented in Fig. 1 .

The BookRoll system interface of the Limnic Eruption lesson

The instructional design of the SETM lessons are presented in Appendix 1 .

In order to make the tasks proceed more smoothly, we designed five questions for students to discuss with each other. (1) Where was CO 2 from in Lake Nyos? (2) Where did more CO 2 dissolve and accumulate in the Lake Nyos, the surface or the bottom? Why? (3) According to the Wikipedia, there is about 90 million tons of CO 2 dissolved in the Lake Nyos (Wikipedia: Lake Nyos , Japanese version). How much pressure does it take for 90 million tons of CO 2 to dissolve in Lake Nyos at 20 °C? (4) In summary, what is the mechanism of limnic eruptions? (5) Could Limnic Eruption possibly occur in Japan?

The process of the lesson Limnic Eruption was presented in Fig. 2 . Before each lesson, students were required to read the related learning materials and to highlight and record the contents they did not understand or thought those were important. In the first week, the teacher introduced the Limnic Eruption disaster, and explained the difficult contents according to what students highlighted (pre-learning). And after that, students worked on question 1 and 2 by searching and analyzing the information, and discussing with others about the questions. Students were asked to talk about the highlights and annotations they added during pre-learning. Then, all groups would make a presentation about their conclusion and received feedback from the teacher and other students. During the second and the third week, students preformed pre-learning and group discussion in the same form, and worked on the questions 3~5. In the fourth week, students were asked to use the integrated knowledge and skills to design a manual of disaster mitigation and make a presentation to the whole class.

The process of the lesson Limnic Eruption

Design and procedure

This study was conducted in a tenth-grade science class at a private senior high school in Japan with the participation of 12 students. The period of this study was between November and December 2018 and included four lessons (50 min per lesson) over 4 weeks (one lesson per week). In addition to the teaching hours, students were also asked to read the provided learning materials on the BookRoll system, and to finish the assignments.

Before the lesson, students were required to take a Collaborative Problem Solving questionnaire (hereinafter, the “CPS Questionnaire”), which concerned their prior awareness of whether and how to use CPS skills in typical science classes as the pre-questionnaire, and a pre-test to check their prior knowledge. After the completion of the STEM lesson, students received the same CPS Questionnaire and a new STEM Learning Strategy Questionnaire (hereinafter, the “SLS Questionnaire”) as post-questionnaires. The CPS post-questionnaire was conducted to assess the change in students’ CPS awareness before and after the STEM lesson, while the SLS questionnaire checked the kind of learning strategy students used during the STEM lesson, and a post-test was also conducted to see whether their related knowledge had changed.

Data collection

In order to investigate factors that might affect the cultivation of CPS skills, we examined the relationships between CPS awareness and learning performance, the STEM learning strategy (SLS) used, and the learning behaviors in learning scientific materials during individual pre-learning and collaborative work.

Therefore, we collected data from three tools: questionnaires, tests, learning logs, and the dialogue during group discussion. The CPS Questionnaire was designed with reference to the CPS framework proposed by Hesse et al. ( 2015 ), which contains 17 items in total (see Appendix 2 ). The CPS pre-postquestionnaires contain two dimensions, social skills and cognitive skills, and include five factors, Participation , Perspective Taking , Social Regulation , Task Regulation , and Learning and Knowledge Building . These factors could reflect how the students perceived the quality of collaborative activities and their cognitive process when carrying on tasks. The CPS questionnaire has been used in previous studies (Chen et al., 2018 ) to examine students’ awareness of collaborative and cognitive activities, showing that the questionnaire was reliable. We also assessed the reliability, and the overall Cronbach’s α value of pre-CPS Questionnaire was 0.77 (the reliability of Participation , Perspective Taking , Social Regulation , Task Regulation , and Learning and Knowledge Building was 0.83, 0.79, 0.79, 0.71, 0.73 respectively), and post-CPS Questionnaire was 0.79 (the reliability of each factor same as above was 0.78, 0.84, 0.75, 0.84, 0.74 respectively).

The SLS Questionnaire developed by Griese et al. ( 2015 ) was used as the post-questionnaire, concerning students’ learning strategy during their STEM learning. The SLS Questionnaire contains nine factors, Organizing , Elaborating , Repeating , Effort , Attention , Time Management , Learning Environment , Peer Learning , and Using References , and includes 27 items (see Appendix 3 ). We translated the SLS Questionnaire to Japanese and made minor changes to the items to make them more suitable for senior high school students. The overall Cronbach’s α value of SLS Questionnaire was 0.78 (the reliability of Organizing , Elaborating , Repeating , Effort , Attention , Time Management , Learning Environment , Peer Learning , and Using References was 0.86, 0.73, 0.78, 0.71, 0.82, 0.84, 0.88, 0.70, 0.71 respectively).

The contents of the CPS Questionnaire and SLS Questionnaire are shown in Table 1 . Both questionnaires were rated on a Likert scale from 1 to 5 (1. Strongly disagree ; 2. Slightly disagree ; 3. Neither ; 4. Slightly agree ; and 5. Strongly agree ). Free text space was also provided on the post-questionnaire to collect students’ individual reflections and their impression of STEM lesson.

The pre-posttests contained the same ten questions on students’ acquisition of CO 2 -related knowledge. The tests included seven conceptual multiple-choice questions concerning the nature (two questions) and solubility of CO 2 (five questions), one calculation question, and two application questions that required students to solve problems related to solubility of CO 2 and disaster reduction consciousness. Thus, the results of the tests can reflect the change of students’ conceptual understanding of the CO 2 -related problems, and the ability to transfer their knowledge to solve the problems. The conceptual questions were the same level as their final examination, and calculation question and application questions were more difficult than their final examination.

The learning logs of the operations students performed when reading and understanding digital learning materials through BookRoll system were collected, yielding data on their frequency of turning to the next/previous page and adding/deleting markers, annotations. The learning logs both in-class and out-of-class were collected in this study.

Results and discussion

Research question 1: What factors of STEM learning strategy and learning behaviors have relationships with the change in learning performance in STEM learning?

Changes in learning performance

The pre-posttests consist of ten questions (worth ten full marks) about knowledge related to CO 2 and disaster reduction. Due to the small sample size of the study, we looked at histograms of students’ pre-posttests with the normal curve superimpose, and histograms showed obviously not symmetric, moderate tailed distributions, which indicated apparent non-normal distributions of data. Therefore, we adopted non-parametric Wilcoxon rank test to assess the significance of the change of pre-posttests concerning students’ changes of related knowledge.

As shown in Table 2 , the mean value improved from 3.42 ( SD = 1.24) to 5.92 ( SD = 1.51) at a significance value of 0.01, which shows statistically significant differences in students’ learning performance during the STEM lesson.

In order to investigate whether learning strategy and learning behaviors of reading scientific materials had influence on learning performance, we used Spearman’s rank correlation coefficient to assess the correlations between learning performance from the data of tests on the one hand and learning strategy from the SLS Questionnaire and learning logs for the reading digital learning materials on the other.

Correlations between changes in learning performance and STEM learning strategy

First, we analyzed the correlations between changes in learning performance and the SLS (see Table 3 ); however, no correlation was found.

Lou et al. ( 2011 ) suggest that students should be guided efficiently to immersion in STEM learning. However, in this lesson, the instructor played the role of a facilitator who only controlled the flow of the lesson and provided advice or gave answers directly when necessary, indicating that efficient guidance for STEM learning is not enough in this instructional design.

As Kelley and Knowles ( 2016 ) pointed out, as an important factor in STEM education, both educators and learners should put emphasis on the integration of STEM subjects. In order to make the integrated approach more effective in conveying to students how STEM knowledge can be applied to real-world problems, it is necessary for students to think and understand the relevant ideas in the individual disciplines and multi-disciplinary integration (Kelley & Knowles, 2016 ). However, the results of the students’ free text questionnaire suggests that they only focused on scientific or mathematical knowledge and on how to use it to solve a specific provided problem, rather than thinking of relevant ideas or integration. This is one possible reason why students’ STEM learning strategy failed to help them to improve their learning performance.

Correlations between changes in learning performance and learning behaviors

Regarding the correlations between changes in learning performance and the learning behaviors involved in reading digital learning materials, we divided learning behaviors into two parts, pre-learning and group discussion. The results are presented in Table 4 .

During pre-learning, there was no correlation between changes in learning performance and learning behaviors, and we consider the same reason to be involved here as above, the lack of understanding and thinking about STEM learning methods, as well as guidance from instructors in STEM learning.

Turning to learning behaviors in group discussion, this included students’ operations when reading digital learning materials. Some functional tools such as markers, annotations indicate behaviors associated with students’ ways of thinking (for example, highlighting when they do not understand) and changes in their thinking (deleting markers when they change an idea). The results in Table 4 show that there was a moderate positive correlation between changes in learning performance and Add Marker (ρ = 0.66, p < 0.05), and a strong positive correlation between changes in learning performance and Delete Marker (ρ = 0.76, p < 0.01).

Students frequently add or delete their markers during group discussions because they take others’ contributions into mind when reconsidering problems (Chen, Uemura, Goda, et al., 2018 ), which was also confirmed by our classroom observation. The marker tool is effective in facilitating students’ deeper processing and retrieval if instruction in thinking about what to mark and in questioning when re-reading is provided (Yue, Storm, Kornell, & Bjork, 2015 ); otherwise, it may negatively affect performance on higher-level tasks that require them to make inferences (Dunlosky, Rawson, Marsh, Nathan, & Willingham, 2013 ).

In this STEM instructional design, students were required to highlight the context they did not understand in yellow and the important contexts in red before the lesson, and to discuss these contexts during the lesson. They were also asked to delete the markers if they change their ideas after the discussion. Since no correlation between changes in learning performance with marker-using behaviors was found during pre-learning but was found during group discussion, it can be inferred that marker-using behaviors could facilitate the integration of other contributions into their own thoughts and reconsidering the problems ( Perspective taking ). However, we have no observations supporting this. Thus, the dialogue analysis had been conducted to examine whether students had the behaviors of perspective taking during discussion.

Research question 2: What factors of STEM learning strategy and learning behaviors have relationships with the change in CPS awareness in STEM learning?

Changes in CPS awareness

As with the assessment of the pre-post questionnaires, we also checked histograms of students’ CPS pre-postquestionnaires with the normal curve superimpose, and found non-normal distributions of data. Therefore, a Wilcoxon signed-rank test was used to assess the significance of the change of CPS pre-postquestionnaires concerning students’ awareness of whether and how to use CPS skills. The results are shown in Table 5 .

With respect to Social Skills, Perspective Taking was increased from 13.25 ( SD = 2.18) to 13.67 ( SD = 1.37), while Participation decreased from 12.17 ( SD = 2.08) to 11.92 ( SD = 1.88) and Social Regulation decreased from 12.17 ( SD = 2.44) to 11.58 ( SD = 2.19).

Concerning the Cognitive Skills, Task Regulation improved from 14.50 ( SD = 3.40) to 15.08 ( SD = 3.09), while Learning and Knowledge Building declined from 14.25 ( SD = 2.70) to 13.75 ( SD = 2.18). However, no statistically significant difference was found for any factor, indicating that CPS awareness had not improved through this STEM lesson.

This might be because we designed this STEM lesson according to the CPS process (Hesse et al., 2015 ), which includes the steps Identifying the problem , Representing the problem , Planning and executing , and Monitoring and reflecting , as applied in our earlier studies (Chen, Uemura, Hao, et al., 2018 ). However, according to Hesse et al. ( 2015 ), the important points of the latter two CPS processes are understanding other group members ’ states for Identifying the problem and understanding the group ’ s states for Monitoring and reflecting . In light of these points, in earlier studies we used a Moodle system to help students and the instructor to understand mutual states. However, in the present study, we only used the BookRoll system because we focused on the effect of learning behaviors on CPS cultivation.

In order to determine factors that might affect the cultivation of CPS skills, we used Spearman’s rank correlation coefficient to assess the correlations of CPS awareness from the data of CPS Questionnaires with STEM learning strategy (SLS Questionnaire) and learning logs (reading digital learning materials).

Correlations between changes in CPS awareness and STEM learning strategy

From the results in Table 6 , we can see that there were strong negative correlations of Social Regulation in CPS awareness with Organizing (ρ = − 0.74, p < 0.01) and Using References (ρ = − 0.77, p < 0.01) in SLS, and moderate negative correlations of Social Regulation with Elaborating (ρ = − 0.50, p < 0.1) and Time Management (ρ = − 0.52, p < 0.1) in SLS.

Since the Social Regulation factor refers to strategies recognizing the diversity of group members and negotiating with them, the point of this factor is communication with others. When conducting STEM education, it is necessary for STEM educators to provide students with multidisciplinary, multi-perspective viewpoints and a collaborative approach that links them with a broader community (Kelley & Knowles, 2016 ; Kennedy & Odell, 2014 ). However, the SLS in this study focused on individual learning (except in the case of Peer Learning ) and found that individual SLS negatively affected communication during group work. Furthermore, we did not provide training or guidance on how to learn integrated STEM subjects, so future research should consider how to guide students to master STEM learning well and integrate individual learning strategies, especially these four factors, efficiently into collaboration.

Correlations between changes in CPS awareness and learning behaviors

Next, we assess the correlations between CPS awareness and learning behaviors concerning how students read digital STEM learning materials. The results are presented in Table 7 .

During pre-learning, moderate negative correlations were found for Learning and Knowledge Building in CPS awareness with Prev (ρ = − 0.55, p < 0.1), Add Marker (ρ = − 0.60, p < 0.05), and Delete Marker (ρ = − 0.54, p < 0.1) of learning behaviors.

In an earlier study, we concluded that students’ behaviors of frequently changing pages or turning to the previous page imply a lack of familiarity with the learning contents and cause them difficulties in constructing knowledge. They added markers to what they thought was important or thought they understood and deleted markers when they changed ideas, from which it could also be inferred that they understood the contents poorly and thus changed their minds easily. However, the Add Marker logs we collected included both yellow (not understand) and red (important) highlights, meaning that we could not identify which part of their behaviors actually negatively affected their knowledge building.

In group discussions in STEM lessons, there were moderate positive correlations found between Social Regulation in CPS awareness and Add Marker (ρ = 0.60, p < 0.05) and Delete Marker (ρ = 0.64, p < 0.05) of learning behaviors.

During group discussion, we observed that when students discussed the problems as well as the contents they did not understand, they deleted the old markers when they accepted others’ opinions while adding new markers. Therefore, the behaviors of adding and deleting markers indicate they paid attention to communication and negotiation in group work, showing that the effective utilization of the marker tool could facilitate the social regulation in STEM lessons. And students’ behaviors of social regulation during discussion would be examined by dialogue analysis.

In this study, we used questionnaires to investigate how students used SLS but did not provide instruction or guidance in how to use STEM learning strategies efficiently. Therefore, we assessed the correlations between SLS and learning behaviors of reading scientific materials to find out how their learning strategy affected their actual learning behaviors, which could help our future instructional design in ways of using SLS.

According to the results in Table 8 , in students’ pre-learning, a moderate positive correlation was found between Attention of SLS and Add Annotation learning behavior (ρ = 0.54, p < 0.1), while there were moderate negative correlations between Learning Environment of SLS and Add Marker (ρ = − 0.54, p < 0.1), Delete Marker (ρ = − 0.52, p < 0.1), and Add Annotation (ρ = − 0.51, p < 0.1).

Since students were required to take notes about the problems provided when they read the materials, it is understandable that annotation would help them to concentrate on the contents related to problems and solutions in STEM learning.

However, it would hurt their learning performance if students only took dictation word by word rather than taking notes with conceptual understanding and thinking (Mueller & Oppenheimer, 2014 ). No guidance in using functional tools seems to be one reason for the negative correlations between the use of the marker and annotation tools with students’ expected learning environment, where it is easy to concentrate and find references.

As for the group work, moderate positive correlations were shown between Repeating of STEM learning strategy and Next Page (ρ = 0.51, p < 0.1) and Previous Page (ρ = 0.61, p < 0.05) of learning behaviors, of Effort strategy with Next Page behavior (ρ = 0.56, p < 0.1), and of Learning Environment strategy with Add Bookmark (ρ = 0.58, p < 0.1), and a strong positive correlation between Effort and Previous Page (ρ = 0.75, p < 0.01).

On the other hand, there were moderate negative correlations found for Organizing strategy with Add Marker behavior (ρ = − 0.58, p < 0.05) and for Elaborating strategy with Delete Marker behavior (ρ = − 0.55, p < 0.1), and a strong negative correlation between Organizing strategy and Delete Marker behavior (ρ = − 0.74, p < 0.01).

Besides guidance on how to use the functional tools of the BookRoll system, some learning strategies such as organizing/summarizing, elaborating/application, and repeating should also be taught with efficient design under certain learning conditions (Dunlosky et al., 2013 ).

Dialogue analysis

The Spearman’s rank correlation coefficient revealed significant positive correlations between the utilization of marker tool with changes in learning performance (RQ1) and CPS social regulation awareness (RQ2) both during the group discussion. In order to investigate whether students had the behaviors of perspective taking and social regulation, dialogue analysis was conducted to understand how students displayed these skills.

We collected the dialogue data of all groups during the discussion and categorized dialogue thread in relation to perspective taking and social regulation factors with reference to the Hesse et al. ( 2015 ).

According to the CPS framework proposed by Hesse et al. ( 2015 ), there are two elements adaptive responsiveness and audience awareness (mutual modeling), in perspective taking factor, and four elements in social regulation factor, which are negotiation, self-evaluation (meta-memory), transactive memory, and responsibility initiative. The elements and the indicators of perspective taking and social regulation were listed in Table 9 .

Perspective taking—adaptive responsiveness

95. Student 9 (S9): The change of the temperature has increased.

96. S7: You mean the temperature has increased?

97. S9: Because there is difference in the temperature (between two place).

98. S7: Yeah, yeah, yeah, I got it .

99. S9: In the lake.

100. S7: So difficult to accumulate (CO 2 )?

101. S9: Because water constantly circulates through.

102. S7: Yes, yes, yes.

118. S10: So back to the question, what about in Japan?

119. S9: See the beginning this chapter (of the learning materials on BookRoll system), there is huge feature in Cameroon, that is landslide. Especially in summer, it rains almost every day.

120. S10: Yeah, yeah, yeah.

121. S9: Landslides occur easily (in Cameroon), but landslides don’t occur often in Japan.

122. S10: Yes, that ’ s true.

Adaptive responsiveness includes the indicator “ignoring, accepting, or adapting contributions of others.” As shown in example 1, in Line 98, S7 used “I got it” to express his agreement with S9’s explanations, which means he has accepted other’s viewpoints, and reconsidered the problems (in Line 100). Similarly, in Line 122, S10 used “Yes, that’s true” to show his agreement with S9’s viewpoint. Since S9 provided his explanations based on the learning materials on BookRoll system (in Line 119), it could be inferred that the S9 did have the behaviors of Adaptive responsiveness during the utilization of BookRoll system, which is consistent with the findings of relationships between marker tool utilization with changes in learning performance (in RQ1).

Perspective taking—audience awareness (mutual modeling)

63. S4: Is Japan different from Cameroon?

64. S5: Of course different, like precipitation.

65. S6: Completely different.

66. S5: (3s) Look at this (the learning materials on BookRoll system), this is different from this, but in summer, precipitation is not that different.

67. S4: Yeah, I see.

68. S6: Because the Japan’s temperature is similar with Cameroon? Like June

69. S4: That’s true.

76. S6: As for Cameroon, where is its location? Around the sea?

77. S5: No, it isn’t. (15s) (Searching the information on the Internet). Here .

78. S4: Yeah, that’ the point. The locations are different.

79. S5: And there is information of Cameroons’ temperature here.

80. S4: Is Cameroon above the equator?

81. S6: About the location, (8s) look at the first page (of the learning materials).

82. S7: (20s) Yeah, I see. I think we should work on the problem now.

According to CPS framework, the indicator of Audience awareness ( mutual modeling ) is “awareness of how to adapt behavior to increase suitability for others.” In Example 2, when others had difficulties in understanding certain contents, some students chose to utilize the additional reference or information to explain the contents.

For example, in line 64 and 65, S5 and S6 answered S4’s question, but did not received any feedback, which means S4 did not accepted their answers well. So S5 waited for 3 s and chose to use learning materials to explain the question (in Line 66). And he also searched for additional information for S6’ question (in Line 77). It is indicated that he had adapted own behavior based on the feedback and understanding of recipient.

Based on their behaviors of providing additional information by BookRoll system (for example, in Line 66, 81), it can be inferred that students utilized BookRoll system tools with their audience awareness.

Social regulation—negotiation

25. S2: Usually, the water in that lake, doesn’t circulate .

26. S1: I think it does .

27. S2: No , it doesn ’ t .

28. S1: Why ?

29. S2: The precipitation is high.

30. S1: That’s why I think it circulates . Because even the precipitation is low, the water would circulates in the lake. I understand what you are taking about , but it is strange that the water doesn’t circulate, even the precipitation is high.

The Example 3 showed a whole negotiation part during the group discussion around “whether water circulates in the lake” issue. Negotiation was conducted here in order to “reaching compromise.” In Line 25~27, S1 and S2 expressed their opinions respectively. After that, S2 asked the reasons (in Line 28), and made comments on the difference between their viewpoints, and proposed reasons to persuade S2 to achieve the agreement (in Line 30).

Although students’ behaviors of negotiation were found during CPS learning, it was not clarified whether their negotiation behaviors had relationship with the utilization of BookRoll tools.

Social regulation—self-evaluation (meta-memory)

10. S1: (Searching the information on the Internet) What about Cameroon…There is only English version, I am over.

17. S1: It is too terrible.

18: S3: About what?

19. S1: My English.

The Self-evalution behavior had not been executed often, and was be found in only one group about the evalution on his English ability.

Social regulation—transactive memory

There was no Transactive memory behavior found in all groups.

Social regulation—responsibility initiative

10. S1: (Searching the information on the Internet) What about Cameroon…(10s) There is only English version, I am over.

11. S1: (Reading out the contexts on the BookRoll system) (15s) Yes, I agree.

12. S2: How dangerous would it be? The limnic eruption.

13. S1: (11s) It has be written here.

46. S2: What about searching on the Internet?

47. S3: Good idea.

48. S1: Let’s search for the history of Cameroon on Wikipedia.

The indicator of Responsibility initiative is “assuming responsibility for ensuring parts of the task are completed by the group,” such as conducting activities and reporting to others, assuming group responsibility as one’s own responsibility. In Line 11 and 13, S1 had investigated certain information and reported it to other members. And in Line 46 and 48, S2 had proposed the activities which should be conducted by group members, S1 accepted that responsibility and taken it as his own responsibility. Moreover, the Responsibility initiative behavior was found during the utilization of BookRoll system (in Line 11), which was consistent with the results of RQ2 (relationship was found between marker tool utilization and CPS social regulation).

The purpose of this study was to investigate the effect of several variables, including students’ learning strategy for STEM and learning behaviors when reading scientific materials online, on their learning performance, and cultivation of CPS skills.

The results of this study showed that different SLS and learning behavior variables would affect students’ learning performance and CPS awareness in different ways. We summarized the relationships between factors of this study with two figures.

As shown in Fig. 3 , concerning changes in learning performance, the results implied that the frequency of Add and Delete Marker behaviors in group discussion would have positive influence on students’ learning scores. Since we only analyzed the correlations between certain variables, it can also be inferred that students whose learning performance improved more showed a tendency to use marker tools more frequently. Both explanations indicate that marker tools can be used effectively in STEM learning performance improvement.

The results of Research Question 1&2

As for the CPS awareness, some factors of SLS, including Organizing , Elaborating , Time Management , and Using References , showed negative correlations with Social Regulation of CPS social skills. Moreover, negative correlations were also found between Learning and Knowledge Building of CPS cognitive skills and Add and Delete Marker and Previous Page behaviors in students’ pre-learning. These results imply a deficiency in and necessity for guiding students in how to use STEM learning strategies or functional tools efficiently and integrate them into collaborative activities. The positive correlations found for Add and Delete Marker in group discussions with Social Regulation of CPS social skills also indicate the effectiveness of marker tools.

Furthermore, in order to determine how to support students in utilizing their SLS, we assessed the correlations between students’ SLS with their actual learning behaviors (results were summarized in Fig. 4 ), finding in pre-learning a positive correlation between Attention strategy and Add Annotation behaviors, and negative correlations between Learning Environment strategy and the Add and Delete Marker and Add Annotation behaviors.

The results of Research Question 3.Factor without significant relationship with other factors were omitted in this figure. ** p < 0.01, * p < 0.05, † p < 0.1

In group discussion, there were positive correlations between Repeating and Effort strategies and Next and Previous Page behaviors, respectively, while negative correlations were found for Organizing strategy with Add and Delete Marker behaviors and for Elaborating strategy with Delete Marker behaviors. These results suggest the value of teaching these SLSs using functional tools.

Based on the results above, we would give some suggestions for CPS STEM learning linked to the research questions. From the results of RQ1, when participating in collaborative activities in STEM learning, marker tool is considered to be effective in STEM learning performance improvement, because of its advantages such as helping students focus on the discussion topic and integrating contributions from others into their own thoughts.

From the results of RQ2, certain factors in STEM learning strategy should be executed by individual, such as organizing, elaborating, time management, and using reference. Besides, it is also suggested that marker tool is useful in students’ group work including communication and negotiation.

From the results of RQ3, although mark tool is shown to be useful in collaborative activities, it does not help students in knowledge building and knowledge organizing and elaborating without detailed guidance about how to utilize such tool.

Limitations and future work

The first limitation of our study is the small number of participants; as mentioned above, only 12 senior high school students participated in the STEM lesson, which makes our study results hard to generalize. Thus, this study aims at providing the direction to take the behavioral and strategical factors into consideration in CPS-based STEM learning. In our future work, we should increase the sample size to gain a more generalizable conclusion, and provide the specific model for CPS-based STEM learning. Furthermore, it is also considered to use other statistical approach such as SEM or path analysis, to further explore cause-effect relationships between learning performance and CPS awareness with learning behaviors and learning strategies in STEM education.

Second, we have conducted two previous studies on instructional design for improving CPS skills following CPS process; two previous studies both indicated that some of the factors in CPS awareness, however, not all experiments showed the same results in the improvement. As for the present study, although we designed this STEM lesson following the same CPS process, the CPS process was not supported with technology as in our prior studies, which led to our finding no statistically significant differences in the improvements in all factors of CPS awareness. Therefore, considerations should be paid to the issue of how to support students’ CPS learning, such as expand the sample size to gain more representative results and improve the future study, or supporting CPS process with some the help of technology.

Finally, as many results in the present study indicate, it is important to provide students with training or guidance in applying STEM learning strategies and functional tools, especially marker and annotation tools, which should also be taken into consideration in our future research.

Availability of data and materials

All data generated or analyzed during this study are included in this published article.

Abbreviations

• Collaborative problem solving
• Learning analytics

Learning management system

STEM learning strategy

Science, technology, engineering, mathematics

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Acknowledgments

This study is funded by Japan Society for the Promotion of Science(JSPS) (Grant Number: JP16H06304, JP16H03080, JP19H01716), and Cross-Ministerial Strategic Innovation Promotion Program from Cabinet Office.

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LC and MY designed this research overall. LC and YG designed this instruction. YG contributed to modify the instructional design. NY is science teacher of the high school. He conducted the class. YG was engaged in considering the evaluation method of this study. FO, YT, AS, SK, HO developed and deployed the learning analytics platform. MO, YG, and MY advised the improvement of the instructional design from the viewpoint of cognitive science. MY supervised this research. All authors read and approved the final manuscript

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Drs. Ogata and Yamada received research grants from JSPS for this research project. Drs. Goda, Okubo, Taniguchi, Oi, Konomi, Shimada, and Yamada received research grants from JSPS for other research project. Drs. Ogata and Shimada received research grants from Cross-Ministerial Strategic Innovation Promotion Program from Cabinet Office. Ms. Li Chen and Mr. Yoshimatsu do not have any conflict on this research.

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Instructional design of the Limnic Eruption STEM lesson

The description of the case

There is a crater lake named Lake Nyos in Cameroon. In 1986, massive amounts of dissolved CO 2 suddenly erupted from the bottom of the lake, causing a limnic eruption. The eruption led to the death by asphyxiation of around 1,700 people in a nearby village. There is still concern about such gas hazards recurring.

The purpose of the lesson

Investigating the mechanism (reason) of the limnic eruption and designing a manual of disaster mitigation, including knowledge of natural disasters, the relationship between the environment and human beings, and what to do when faced with such natural disasters.

Four STEM domains in this lesson

Science (S): Students need to understand CO 2 -related scientific knowledge, including the nature and generation process of CO 2 and the solubility of CO 2 , and use scientific knowledge and skills to solve the applied problems.

Engineering (E): Students are asked to design a manual of disaster mitigation in order to integrate their multidisciplinary knowledge and skills to problem-solving and improve their understanding of scientific and mathematical knowledge by practicing their application.

Mathematics (M): Students are required to use their mathematical knowledge and skills to perform the respective calculations in CO 2 -solubility-related problems and make a judgment as to whether the information provided by the Internet is correct.

Technology (T): Students are asked to use a M2B (Moodle, Mahara, and Bookroll) system to support individual pre-learning and group discussion and the Internet for information retrieval. The use of technology and decision-making regarding Internet information is expected to improve students’ technological literacy.

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Chen, L., Yoshimatsu, N., Goda, Y. et al. Direction of collaborative problem solving-based STEM learning by learning analytics approach. RPTEL 14 , 24 (2019). https://doi.org/10.1186/s41039-019-0119-y

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Developing Collaborative Skills through STEM Approach

Submitted: 14 July 2023 Reviewed: 31 October 2023 Published: 24 November 2023

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Collaborative learning is a practice that dates back centuries. In Confucius’ classic text, Li Ji, there is a famous saying that one becomes narrow-minded when learning without friends. Therefore, collaborative skills not only allow students to interact with others but also enhance their opportunity to learn, which differs significantly from individual learning. Collaborative problem-solving is crucial in science, technology, engineering, and math (STEM), which are fraught with complex challenges like climate change, overpopulation, welfare, resource management, health, and biodiversity. The Systematic Literature Review (SLR) approach was used to find, assess, evaluate, and interpret all of the research that was accessible in order to create this book chapter. Preparing the youths with a collaborative mindset is crucial for addressing the issues and difficulties that arise in real life. This review specifically focuses on developing technology-based collaborative skills through STEM approach to reflect the trend of integrating technology into education.

• collaborating skills
• STEM approach
• Systematic Literature Review

Author Information

Chairil faif pasani *.

• Universitas Lambung Mangkurat, Indonesia

Rizky Amelia

*Address all correspondence to: [email protected]

1. Introduction

The scale of collaborative skills can range from as small as a couple to a large community. The timing can vary, such as in a year-long course or a one-time activity (for example, a team-building activity in a company).

The setting can change from physical collaborative skills to virtual learning.

The medium can include various technology platforms and others [ 6 ].

Besides these pragmatic parameters, the dynamics of collaborative skills are influenced by various forces, such as the preference for individualism or collectivism in relation to learning objectives. When learning goals are established for individuals, collaborative skills prioritize improving individual knowledge or skills. In the Programme for International Student Assessment (PISA) test, the Organization for Economic Co-operation and Development (OECD) defines students’ collaborative problem-solving competence as the capacity of individuals to effectively engage in a process, where two or more agents attempt to solve a problem [ 7 ]. This entails the collaborative sharing of knowledge, skills, and efforts in order to collectively work toward the attainment of a solution.

In summary, when education is focused on communal objectives, the emphasis is placed on fostering community cohesion and collaborative knowledge development. The pedagogical approach known as ‘team-based learning’ centers on fostering small group dynamics within a classroom environment, wherein tasks are designed to facilitate both individual learning and team growth [ 8 ]. Similarly, advocates of ‘communities of practice’ [ 9 ] or knowledge building [ 10 ] emphasize the importance of building communities among students. Communities of practice are groups of people who share a common concern or passion for what they do, and regularly interact to learn how to do it better [ 9 ]. Knowledge building emphasizes collective knowledge creation and innovation as well as prioritizes the advancement of community knowledge over individual achievement [ 11 ].

Another force that influences collaborative skills is the division of work. In many learning scenarios, the teacher organizes the work division, where tasks are divided into independent sub-tasks, and the group assembles the different parts together [ 12 ]. Collaborative learning is often utilized in these cases. On the other hand, in other collaborative skills opportunities, work is not divided but rather negotiated and completed by the individuals involved [ 13 ].

All group members contribute to the same learning task in a collaborative learning environment. In contrast, inside a competitive academic setting, pupils tend to operate autonomously and exert efforts to surpass their peers. Nevertheless, it is important to note that collaboration and competitiveness are not inherently contradictory concepts. Contradictions can play a pivotal role in propelling the development of an activity system [ 14 ]. Similarly, competition can be a positive element for collaborative skills [ 15 ]. This often occurs alongside collaboration within or across group learning settings [ 16 ]. A competitive mindset can be a double-edged sword and can facilitate collaborative problem-solving processes when carefully crafted and well-utilized.

Many curricula, instructional guidelines, and instructional materials connected to STEM have emerged in recent years. Even though the majority of the early attempts at STEM education focused on one or more of the STEM topics alone, proponents for stressing links between or among the subjects in STEM education are growing [ 17 ]. Due to the earlier lack of integration, STEM-related research frequently has a wide variety of foci and contexts. While other more thorough studies may address additional and deeper relationships among the STEM disciplines, the study contexts of some STEM-related studies may only involve one of the STEM fields [ 18 ]. The potential benefits of using educational technology to enhance STEM learning outcomes are being emphasized by educators and academics more and more as a result of the rapid growth of information and communication technology [ 5 , 19 ]. This study provides compelling evidence that collaborative skills effectively address critical issues in STEM education.

The Systematic Literature Review (SLR) approach was used to find, assess, evaluate, and interpret all of the research that was accessible in order to create this book chapter. With this approach, the author conducts systematic journal reviews and identifications, going through each stage in a set order. Researchers gathered journal articles from Google Scholar, Research Gate, SINTA, DOAJ, and Scopus to finish this manuscript. STEM education and collaborative education are key terms.

2. Benefits of collaborative skills in STEM education

Collaborative skills have been well-embraced in STEM education [ 20 , 21 ]. It is advisable to establish student learning communities as a means to enhance student engagement and perseverance in STEM programs inside educational institutions [ 22 ]. Multiple meta-analysis studies have demonstrated that the acquisition of collaborative abilities has a positive impact on overall student learning [ 4 , 23 ]. Moreover, a more recent meta-analysis revealed that collaborative skills supported by computer technology have demonstrated notable effectiveness in STEM education, specifically in process, knowledge, and affective outcomes [ 24 ].

The study presents substantial evidence that collaborative skills are an effective means of addressing crucial challenges in STEM education [ 25 ]. An issue that can be identified is the phenomenon of low enrolment coupled with great interest [ 22 , 26 , 27 , 28 ]. The cultivation of collaborative skills possesses the capacity to enhance students’ self-esteem and sense of accomplishment through the facilitation of their assistance to others and engagement in the co-creation of classroom activities. Students can develop HOTS (Higher Other Thinking Skills) and learn better in STEM content through collaborative skills than individual learning [ 29 ]. In addition, collaborative skills can foster students’ communication skills to resolve conflicts [ 30 ].

Another issue is equity in STEM education. Female students generally have lower enrollment rates but higher dropout rates in STEM majors compared to male students. Similarly, students from low-income families show similar patterns compared to high-income families [ 26 , 27 ]. Previous studies have shown that students’ satisfaction with the positive experience of collaborative skills makes them more intrinsically interested in learning. This has consequently made them more willing to attend school and persist in STEM learning. In addition, the cultivation of collaborative abilities facilitates effective communication and interdependence among individuals within a group, irrespective of their racial, gender, or academic backgrounds. This, in turn, contributes to the establishment of a learning environment that is characterized by fairness and equality [ 31 , 32 ].

3. Collaborative skills and technology in STEM education

The utilization of technology is of paramount importance in enabling the development and enhancement of collaborative abilities. The utilization of visual representations in educational contexts can facilitate the comprehension and completion of learning activities, facilitate collaborative processes, and function as a supportive framework for the construction of shared knowledge [ 33 ]. This study centers its attention on instances of technology that provide direct support for collaboration within the learning process. While several technologies have the potential to support collaborative abilities, their impact is not considered important. This assertion is substantiated by the findings of Lee et al. (2016) in their report, wherein students engaged in the collection of their activity data, such as step counts over a specific duration, through the utilization of sports watches. Subsequently, these students acquired statistical knowledge by collaboratively examining and analyzing the collected data within group settings [ 34 ]. The sport watches were solely used for data collection, and not to facilitate collaboration. This type of study is beyond the scope of this chapter.

4. Developing collaborative skills through STEM approach

4.1 the environment of remote collaborative skills.

Audio and video conferencing platforms, such as Zoom and Skype, are extensively utilized in contemporary synchronous remote learning environments. The aforementioned tools facilitate the connection of students in different locations [ 35 ]. They frequently incorporate distinct functionalities that enhance collaboration abilities, such as screen sharing, chat rooms, and annotations. Babaian and Schiano presented a set of practical recommendations aimed at enhancing the effectiveness of small-group collaboration within the context of online learning [ 36 ]. The aforementioned skills encompass the ability to facilitate teachers and students in becoming acquainted with functionalities that facilitate small-group collaboration. This entails effectively managing display names and altering screen names to reflect group identification or names, seamlessly transitioning between group workspaces and the primary space, communicating messages to all groups or specific groups within breakout spaces, and possessing a comprehensive understanding of sharing and annotation tools.

Furthermore, research has been performed to investigate these characteristics. Singhal (year) demonstrated the utilization of breakout rooms within the Zoom platform for the purpose of conducting a virtual pharmacotherapy course throughout the COVID-19 pandemic [ 37 ]. In class, every five to six students were grouped and placed in a breakout room, hence the teacher could move around the room to facilitate discussions. The study emphasized that the use of breakout rooms can assist students’ small team tasks in promoting active learning, consequently leading to more participation.

4.2 Multi-user virtual environments (MUVEs)

MUVEs have been used to facilitate collaborative skills. In these environments, students typically use avatars to interact with each other in a virtual world, similar to multiplayer online role-playing games [ 38 ]. Additionally, these environments have the potential to provide educational advantages for students diagnosed with autism spectrum diseases [ 39 ]. River City is an exemplary instance in the field of science education, as it serves as a pioneering model for a Multi-User Virtual Environment (MUVEs) learning platform. This innovative educational tool has been specifically developed to effectively involve students in middle grades in the application of scientific inquiry techniques that align with established criteria ( https://muve.gse.harvard.edu/ ) [ 40 ].

The curriculum was enhanced through several development versions [ 41 ]. For example, the first unit focuses on biology and ecology. Students can connect through avatars, and investigate an authentic problem (e.g., disease outbreak in a city) in a virtual city with a river running through the environment. In order to solve a problem, students can collaborate by sharing information and communicating with team members about the collected data through a chat text tool. In retrospect, [ 42 ] concluded that MUVEs have been identified as effective instruments for facilitating student-centered collaborative inquiry learning, offering a viable alternative to traditional inquiry-based science instruction methods.

In this study, Ibanez et al. introduced a novel 3D virtual collaboration skills model designed to facilitate and direct students’ collaborative activities within Multi-User Virtual Environments (MUVEs) [ 43 ]. The model establishes synchronization learning points to achieve individual or group desired learning goals, as well as work division to promote positive interdependence. Based on the model, students conducted a case study in which participants (through avatars) navigated a 3D environment and collected information related to available theater performances in order to purchase tickets for the shows they wanted to attend. They also performed several collaborative activities, such as information sharing and group assessment. The factors affecting interactive collaboration were identified in the first iteration and the design was refined. This resulted in improved collaboration in the second iteration [ 43 ].

Unlike the examples mentioned earlier that gave a game-like setting, [ 42 ] described a geometry unit for fifth graders, wherein students engaged in various activities within a virtual classroom. In this environment, they were able to manipulate virtual 3D geometric objects, observe their classmates’ problem-solving processes, as well as provide feedback and critique to each other. According to their findings, the experimental group outperformed the control group in terms of geometry learning. Moreover, the most effective way for students to acquire abstract geometry ideas is to collaborate with their classmates and practice manipulating virtual objects (e.g., by viewing and critiquing each other’s answers) [ 44 ].

It is noteworthy that manipulating virtual items can enhance peer-to-peer interactions in real life. Jackson et al. designed a math game activity on a table to augment and reinforce what students had learned in their fourth-grade math class [ 27 ]. The students were expected to use components from their resource pool to fill in the blanks on a math problem displayed in the center of the table screen (allowing multiple users to edit the content simultaneously). Four students work together in a group, with each possessing resources that could potentially contain important components of the problem-solving solution. They were required to collaborate in order to complete the task. The students responded positively to the collaborative game-based learning exercise. Upon completing the program, male students’ arithmetic scores showed substantial improvement.

In conclusion, MUVEs and audio/video conferencing systems are examples of mechanisms that connect students online. Although these technologies are now widely used in everyday life, there is still a need for further empirical studies to determine the exact elements of these tools that can most effectively enable collaborative learning [ 45 ]. Meanwhile, MUVEs not only provide alternative and shared areas for students to learn, but they also simulate various work environments [ 46 ]. Students are likely to feel more engaged in the problem-solving process when they can experience it authentically by exploring virtual environments and interacting with virtual objects.

4.3 Facilitate written discourse in groups

Synchronized discussion forum

In synchronous discussion forums, students can participate in real-time using written text or symbols [ 47 , 48 ]. This type of forum is also known as online chat. Some advanced forms of this forum, designed for educational purposes, incorporate simulated intelligent agents that can offer students real-time feedback on the discussed topics and facilitate communication between participants. According to Wang, Rosé, and Chang, high school students collaborated in pairs in several geology-related brainstorming activities [ 49 ]. They used a synchronous chat program called VIBRANT, which included an integrated virtual agent capable of providing real-time comments.

Asynchronous discussion forum

An asynchronous discussion forum allows students sufficient time to analyze the posts made by their peers and provide more useful comments to the forum. This tool provides the benefit of meeting students’ requests for rapid interaction and feedback. In a study examining the efficacy of collaborative learning in an undergraduate engineering course, participants were instructed to engage in group discussions pertaining to assigned teamwork and afterward submit a collective report derived from their email-based communication [ 51 ].

The web, shared by multiple users, such as blogs, can serve as an asynchronous discussion forum to exchange viewpoints. For example, [ 42 ] used an educational blog to engage 21 high school students in examining the issue of acid rain. A learning community was established for each student on the blog, where they shared their results and commented on their colleagues’ findings. According to the results, students showed increased interest in the activity [ 52 ].

Through their activities on the blog, the students have shown social and emotional support for each other, leading to increased construction of new information. An investigation into how students collaborate was conducted with a total participation of 25 pre-service teachers who used an asynchronous online whiteboard to communicate with each other [ 53 ]. The students were divided into groups of five and tasked with developing an interdisciplinary education module that included elements of math and science. Each member in the group can upload their design ideas, while others can comment, make suggestions for modifications, and rate them. Despite the absence of an initial allocation of tasks by the teacher, the students assumed distinct responsibilities and assumed leadership positions (such as soliciting feedback, contributing knowledge, and exerting control over the topic), which were evenly distributed among team members. This occurred even though there was no initial work division by the teacher.

Discussion forums, both synchronous and asynchronous, have been around for quite some time. However, they are constantly evolving with the emergence of new technologies. In recent years, multimodal inputs like audio or video were incorporated into online discourse platforms, such as the Flipgrid app. Moreover, machine-based learning algorithms have been embedded to support automated and customized student feedback [ 48 ].

4.4 Provide direction for the collaborative processes

The previously mentioned technologies generate more opportunities for students to work together by connecting across different locations and times. Nonetheless, the collaboration may not always be successful due to various reasons, such as inefficient communication, lack of interaction, and inappropriate work division [ 54 ]. Affordable collaborative scripts in computer-based learning environments have been investigated as a means to guide collaborative activities. Rummel, Mulins, and Spada used a total of 106 middle school students to investigate scripted collaborative arithmetic learning. They modified a computer-based teaching program called Cognitive Tutor Algebra to include scripts that allowed users to work together [ 55 ].

After initially working on separate challenges individually, two students eventually teamed up at a computer to address a challenge presented by a script, integrating their respective problems. During the collaboration, additional scripts emerged to encourage collaborative behavior at specific times (e.g., contributing based on independent problem-solving experience, listening to peers, and asking questions for clarification). The results showed students who followed the scripts demonstrated better collaborative skills and were more successful in finding solutions to problems compared to other groups that did not follow the scripts.

Collaborative scripts have been employed for many objectives, encompassing the facilitation of collaborative conduct and serving as a mechanism for peer surveillance. The study conducted by Bouyias and Demetriadis aimed to examine and contrast the impacts of compulsory peer monitoring prompts and fading scaffolding scripts in a collaborative argumentation task inside a computer science educational setting. The students participated in an online argumentation learning environment called iArgue [ 56 ]. Random pairs were formed between 34 students who belonged to two different classes. In the peer-monitoring group, when student A submitted a task, peer B would receive the submission along with a peer-monitoring prompt asking them to check whether A’s argumentation followed the model. In case it does not, student B was expected to indicate where improvements are needed. The monitoring task should be completed before student B can continue their work. Besides the peer-monitoring prompts, consistent scaffolding scripts were also provided (e.g., guidance on argument construction).

Students in the fading group did not receive any prompts for peer monitoring, instead, they were provided with a fading scaffolding script. This means there was less scaffolding after the students’ second post. According to the results, the scripts that encourage peer monitoring significantly improved learning outcomes. In another study that investigated the function of collaborative scripts in argumentative learning, these scripts not only served as peer monitoring prompts but also guided students to systematically analyze their partner’s argumentation and encouraged their argumentative construction. These activities include guiding students to paraphrase, criticize, ask questions, provide counterarguments, and propose new arguments. Another benefit of using collaborative scripts in argumentative learning is that they encourage students to develop their argumentative constructions [ 57 ].

A total of sixty students participated in the study, each of whom worked with a partner on a task that required knowledge from both of their respective fields of study. Specifically, the partnership consisted of two students, one with a background in water management and the other with a background in international development studies. The texts were successful in facilitating the development of argumentation knowledge [ 58 ].

The findings indicate that collaborative scripts have the potential to successfully facilitate and improve collaborative processes through the promotion of collaborative behavior, peer monitoring, and knowledge production. This is particularly evident in small-group collaborations. This is true for collaborative scripts used in online environments. Consequently, there should be greater emphasis on incorporating collaborative scripts into educational practice, and more studies on methods that enable collaboration among larger groups.

4.5 Produce, store, and visualize collective knowledge

Besides expanding opportunities for collaborative work and providing direction for collaborative processes, technological tools and platforms have also been developed to facilitate the generation and sharing of collective knowledge by the groups. An example of this is Wikipedia, an online encyclopedia that has grown over the past 20 years and currently has over 56 million entries available in more than 300 different language editions ( https://en.wikipedia.org/wiki/List of Wikipedias). It has rapidly developed into one of the most popular websites. A concept behind Wikipedia states that anyone can contribute to the world’s most comprehensive encyclopedia. This reflects the latest popular advances in humanity’s pool of knowledge.

Several examples of Wikipedia and similar technologies are used in educational settings at various levels. Pifarre and Kleine Staarman investigated the collaborative processes that occur when elementary school students work together on a science book in the context of a wiki. The study focused on student-generated writing from a dialogic perspective, considering the number of paragraphs, words, sentences, and reasoning relationships [ 59 ]. This analysis was conducted on student-generated texts, and the conclusion suggests that dialogic methods should be used to examine the process of collaborative interaction. This approach can help create more effective pedagogies related to wiki use in educational settings. Furthermore, a student-centered inquiry learning paradigm has been created and implemented for use in college biology classes as part of the WIKIed Biology project, supported by the National Science Foundation [ 60 ].

The students participated by collaboration through the use of Web2.0 technologies, managing and tagging educational resources obtained from the Internet. They collaborated in small groups to create and publish website pages based on their scientific inquiry projects. The results showed that the students significantly improved their understanding of various biological concepts, as well as their ability to think critically and their awareness of the relevance of scientific communication.

Similar to Wikipedia’s crowdsourcing model, “citizen science” can be seen as the practice of delegating various scientific endeavors to the general public. The term “citizen science” is used to describe the practice of nonprofessional scientists (such as data collection) by contributing to the generation of professional and scientific knowledge [ 61 ]. Digital Earth is an initiative that involves individuals in the collection and conversion of information related to the Earth into digital form [ 62 ]. OpenStreetMap ( www.openstreetmap.org ), an example of Digital Earth, is a collaborative effort to produce a free and editable map of the world populated with geographic data provided by individual users. YouthMapper, a student branch of OpenStreetMap, which can be accessed online at www.youthmappers.org , is a global network of students who are actively engaged in collaborative mapping projects using OpenStreetMap. Gama et al. [ 42 ] examined the collaborative mapping experiences of students from three different institutions in Europe, North America, and Africa. The study found that participating in Mapathons events (“mapping marathons”) not only improved students’ technical ability and subject knowledge but also enhanced their engagement as socially responsible citizens [ 42 ].

Knowledge forums, led by Marlene Scardamalia and Carl Bereiter over three decades of study and development, are a pioneering platform for communal knowledge construction in upper-middle-class spaces [ 63 ]. These forums serve as tools to assist and support groups in developing their knowledge. Students compile their knowledge records in relation to specific subjects. One of the strengths of the forum is the provision of multiple scaffolds that students can use to develop their submissions. For instance, students can start a statement by selecting a pre-defined question, such as “My Theory” or “My Understanding Problem,” and can also add comments on top to offer a high-level overview. Empirical studies on the topic have shown that students have found the benefits of Knowledge forums in developing their fundamental, domain-specific, and epistemic literacies [ 64 ].

The iKOS platform is a web-based knowledge organization tool that enables cross-disciplinary collaborative learning in a classroom environment [ 65 ]. The platform allows students to generate, exchange, and organize information. It also has the benefit of incorporating multimodal features [ 66 , 67 ]. In Wiki mode, students can create knowledge entries similar to those on Wikipedia, while they can tag and annotate images or photos in PicTag mode. In mind mapping mode, they can create concept maps to visualize the relationships between big ideas. In Flipbook mode, they can publish their multimodal pages and interactive flipbook to the general public. This activity supports multiple modes of knowledge representation. According to the results, after completing a lesson on nuclear power, prospective science teachers were able to create a rather voluminous knowledge network [ 68 ].

Personal response systems (such as clickers), and polling applications are other technology categories that attract attention. An instructional technique known as Peer Instruction is established to engage students in typically lecture-based classroom conversations. These resources are often effective with Peer Instruction instructional practices. When using clickers or polling software, it is necessary to project and view the distribution of student responses on the screen. This allows students to be motivated to talk to their neighbors about their decisions and thought processes, as the projection of the class’ collective state of knowledge engages them in conversation. This strategy has been proven effective in improving student learning and retention through STEM approach [ 69 ].

The platforms mentioned above provide a space for students to store, create, and exchange collective knowledge with each other. In addition to improving the community and building knowledge by contributing to the collective knowledge, the students can also benefit from developing their individual knowledge and other personal skills in the process.

5. Evaluation of technology-based collaborative skills through STEM approach

Despite its widespread use, there is a relatively limited amount of empirical study conducted in evaluating technology-based collaborative skills. The assessment of collaborative learning gained can be viewed from two different perspectives, namely collaborative outcomes and processes. Collaborative outcomes refer to what the group produces during the learning process. This collective output can be analyzed to infer the collaborative processes or individual understanding of teamwork. Meanwhile, the processes examine the complex dynamics (such as social interactions among group members), which serve as a link between the individual and the group.

In order to develop products, the discourse documented has been utilized in various technology platforms during the collaborative learning process. For example, synchronous or asynchronous discussion forums can record the discourse that occurs between members of a learning community. This provides a rich data source for evaluating and analyzing the collaboration and knowledge construction among members.

Jimoyiannis and Angelaina investigated Community of Inquiry (CoI) and Social Network Analysis (SNA), using a total of 131 blog posts that addressed the issue of acid rain [ 52 ]. Through the use of CoI analysis, community characteristics were examined by classifying publications into three categories, namely social presence (i.e., not involving domain knowledge but for emotional communication and group cohesion), cognitive presence (i.e., involving domain knowledge), and teacher presence. Teacher presence was defined as the presence of a teacher in a publication (i.e., including instruction or scaffolding). The SNA analysis provided a quantitative representation of the engagement level of individuals as well as their social relationships. The amount of direct contact each member has with others may indicate the power distribution among students.

The use of external technologies, such as video or audio recordings, to analyze collaborative processes is frequent. This approach can be used to investigate the collaborative processes. Talentino investigated the use of a mixed reality environment to enhance the study of earth science by high school students. The discourse between students and teachers (e.g., questions and answers) as well as among students, were analyzed and coded using video and audio recordings (e.g., comments, questions, or responses between individual students). The statistics revealed a visible increase in the number of statements driven by student participation. This activity is a usual practice for computer-based learning platforms to track students’ activities as they use the platform (e.g., time stamps, clicking on buttons or pages, editing text) [ 70 ].

Several previous studies have focused on analyzing log data to gain a deeper understanding of collaborative processes within groups. Altebarmakian and Alterman [ 68 ] investigated how elementary school students solve arithmetic problems using an online platform with an intelligent virtual tutor under three different learning conditions, namely collaborative, individual, and combined. They also evaluated the effectiveness of each student’s problem-solving approach within the context of their learning conditions by analyzing log data of students’ interactions with the virtual tutor. This analysis included students’ attempts, errors, and hint requests [ 71 ].

Some studies focus on both collaborative procedures and their products. For example, [ 68 ] investigated the extent to which 29 university students engaged in collaborative work during a computer science and psychology class by analyzing the data recorded through the system and the substance of the posts made by the students. The students worked on their assignments in groups of three to five using an educational blog as their platform of collaboration. They were required to independently draft and upload their answers to the initial problem, comment on posts made by others, respond to questions posed by others, modify their solutions in light of the group conversation, and eventually submit final responses [ 68 ].

The participation of each student in the group project is assessed with respect to their cognitive, social, and behavioral habits during the activity. The measurement of their reading, editing, and commenting behaviors, as tracked by the system, provides insights into their behavioral engagement. The log data also revealed that students’ interaction levels depended on whether they referred to previous statements made by their peers. Subsequently, the level of cognitive engagement was evaluated based on the topics covered in the students’ contributions.

An important observation regarding the assessment component of the study on collaborative learning is that most studies use assessments to test the effects on individual students’ academic or emotional outcomes. However, these tests do not necessarily reflect the development of students’ understanding of collaboration or their ability to work together effectively. Therefore, it is crucial they receive timely and useful feedback on their individual and group efforts when participating in learning activities that require collaboration. Continuous assessment of collaborative learning facilitates meaningful communication, not only among collaborative group members but also between students and teachers. Therefore, students have a more positive experience when participating in collaborative learning.

6. Conclusion

The limitations, strengths, and advantages of collaborative learning through STEM approach are outlined in this chapter. The chapter additionally presents instances of technologies that enhance collaboration skills through many means, including facilitating communication among students across different temporal and spatial contexts, fostering written and multimodal exchanges among students, providing guidance for collaborative procedures, and supporting the development of collective knowledge. Further exploration is needed for formative assessment of students’ collaborative knowledge and practices. In addition, the assessment of students’ collaborative skills should be explored, with a focus on evaluating the effect of collaborative learning on students’ academic and affective outcomes (individual) as well as conducting formative assessment of collaborative knowledge and practice.

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Problem Solving in STEM Education

In this Spotlight...

• Animated Contrasting Cases to Improve Procedural and Conceptual Knowledge in Geometry (AC²inG) (PI: Erin Krupa)
• Building Informed Designers (PI: Blake Hylton)
• CAREER: Noticing and Using Students’ Prior Knowledge in Problem-Based Instruction (PI: Gloriana González)
• COnceptual Model-based Math Intervention Tutor (COMMIT) (PI: Yan Ping Xin)
• DEAP: Developing and Evaluating Assessments of Problem Solving (PIs: Jonathan Bostic, Toni Sondergeld)
• Ed+gineering: An Interdisciplinary Partnership Integrating Engineering into Elementary Teacher Preparation Programs (PI: Jennifer Kidd)
• FLECKS: Fostering Collaborative Computer Science Learning with Intelligent Virtual Companions for Upper Elementary Students (PIs: Kristy Boyer, Eric Wiebe)
• Improving Grades 6-8 Students' Mathematics Achievement in Modeling and Problem Solving through Effective Sequencing of Instructional Practices (PI: Joe Champion)
• Mathematical and Computational Methods for Planning a Sustainable Future (PS-Future) (PI: Margaret Cozzens)
• Mathematical Learning via Architectural Design and Modeling Using E-Rebuild (PI: Fengfeng Ke)
• Proportions Playground (PI: Chandra Orrill)
• Science and Engineering Education for Infrastructure Transformation  (PI: Charles Xie)
• Systemic Transformation of Inquiry Learning Environments for STEM (PI: Ellen Meier)
• Zoombinis: The Implementation Research Study of a Computational Thinking Game for Upper Elementary and Middle School Learners (PI: Jodi Asbell-Clarke)

Additional Resources

Featured projects, animated contrasting cases to improve procedural and conceptual knowledge in geometry (ac²ing) (nsf #1907745).

PI:   Erin Krupa  | Co-PI:  Jon Star Target Audience: Middle school students and teachers STEM Discipline: Mathematics

In our materials, two fictional students each present a unique solution strategy to a geometry problem. Middle school students analyze both methods and discuss similarities, differences, strengths, and weaknesses of each strategy. This research is based on positive outcomes of using contrasting cases to support the learning of algebra. The AC2inG project tests the conjecture that utilizing contrasting cases is a viable strategy for improving the learning of geometry. Further, AC2inG materials will be digital, so students will see the concepts come to life through rich visual animations.

Theoretical Framework:  

• Multiple Solution Strategies :  Mathematics education literature often advocates for the use of multiple solution strategies or tools to approach a mathematics problem (Dhombres, 1993; House & Coxford, 1995; NCTM, 2000; Polya, 1973, 1981; Pólya, 1963; A. H. Schoenfeld, 1983, 1988; Vinner, 1989).
• Contrasting Cases as Learning Tool : Goldstone, Day, and Son (2010) stated, “research has demonstrated that the simple act of comparing two things can produce important changes in our knowledge” (p. 103). There is empirical support from cognitive scientist literature for the use of comparing contrasting examples for learning about business negotiations (Gentner et al., 2003), heat flow in science (Kurtz et al., 2001), children’s learning (Loewenstein & Gentner, 2001; Namy & Gentner, 2002), and in studies of infants 4 to 6-months old (Oakes & Ribar, 2005).
• Contrasting Cases in Mathematics Education : In mathematics education, research on comparing has proven effective in learning: estimation (Star & Rittle-Johnson, 2008, March, 2009), the concept of an altitude for a triangle (Guo & Pang, 2011), and equation solving (Rittle-Johnson & Star, 2007, 2009; Rittle-Johnson, Star, & Durkin, 2012). Aside from the work of Star and colleagues, there are very few experimental classroom-based studies on the effects of the Contrasting Cases approach on gains in students’ knowledge, and only one such study exists in geometry (Guo & Pang, 2011).

Methodology:  Overall, we are utilizing design-based research as we develop and test the materials (Brown, 1992; Cobb, Confrey, diSessa, Lehrer, & Schauble, 2003; Collins, 1992; Schoenfeld, 2006). We used a theory-driven process to design our materials and will use a data-driven process to iteratively refine them (Shavelson & Towne, 2002). Through a series of revisions and implementations, we will test and refine our conjectures about how, and what, students learn when interacting with the geometry cases, with the ultimate goal of improving their procedural and conceptual knowledge.

To obtain the data that will inform our revisions, we will use a quasi-experimental design that is similar to prior Contrasting Cases work in algebra (e.g., Rittle-Johnson & Star, 2007); half the students will be in the Sequential group and the other half in the Compare group. The Sequential group will be presented with a page with one student’s solution to a problem, a discussion sheet, a separate page with another student’s solution, and a second discussion sheet. The Compare group will be presented with the supplemental curriculum that includes: a page with one student’s solution to a problem, a page with another student’s solution to a problem, an in-depth side-by-side comparison sheet of the two solutions, a discussion sheet, and a ‘closure’ page.

All students will be administered lesson-level, unit-level, and course-level pre- and post-assessments. In addition, students will be allowed to write or draw on the AC2inG instructional materials, and all lessons will be audio-recorded. After instruction with the animated contrasting cases, we will conduct and audio-record interviews with small groups of students in order to explore students’ geometric thinking. A mixed-methods approach will be used to analyze the data that is collected in order to provide evidence of effectiveness of the supplemental materials on students’ procedural and conceptual knowledge of geometry.

Current Findings Related to Problem Solving:  We are in the first of three years of the grant. To date, we have created paper-based curricular materials for all 8th grade geometry units: Angles, Volume, Transformations, and Pythagorean Theorem. We have created animation scripts that are currently under the third round of revisions with our digital animator, and we hope to have these finalized in time for the 2020-2021 school year.

Key Challenge:  The most obvious current challenge is the COVID-19 pandemic. When schools closed, we were unable to implement our materials with students. To overcome this, we are prepared to conduct think-aloud interviews with students engaging in our digital materials once those have been finalized.

Products:   Project Website  | Curricular Materials    

Building Informed Designers (NSF #1812823)

PI:   J. Blake Hylton |  Co-PIs:  Todd France ,  Patrick Herak ,  Bruce Wellman Target Audience: Grades 9-12 STEM Discipline:   Infusing engineering content into physics, chemistry, biology, and physical science curricula

Description:  This project is developing engineering problem framing activities for high school science classrooms and associated professional development for teachers. Teacher participants are brought in for a three day workshop, where they receive training on engineering content, complete an engineering design experience, walk through the materials, and work to develop an implementation plan for connecting the engineering content to their regular science content. Teachers meet with a team member for continued mentorship at least twice more as they work to enact their implementation plan. Curricular materials are currently undergoing a transformation towards a hybrid synchronous/asynchronous mode using interactive modules deployed via LMS. Materials are being developed for several different problem contexts, to allow tailoring to regular course content, and with multiple levels of proficiency, to allow students to revisit similar content at a higher level in later courses in the curriculum. In addition to analyzing student artifacts, the project also captures student attitudes about engineering as a career path, using the STEM-CIS instrument, and about engineering design, using a modified version of an established instrument based on expectancy-value theory. The project goal is to explore how students' attitudes and abilities shift through repeated exposure across the high school curriculum.

Theoretical Framework & Methodology:  Student attitudes about engineering design are explored using a survey based on expectancy-value theory. Artifacts are coded to identify key traits and correlated with survey results. Students are also surveyed using the STEM-CIS to explore interest in engineering as a career path. In both cases, trends are examined over time as students encounter additional interventions throughout their high school curriculum.

Key Challenges:  There have been two core challenges encountered on this project. First, cooperation of the district professional development coordinator is critical to ensuring access to teachers. We have learned that a formal documentation of this agreement and the expectations as to when teachers will be allowed to participate in project-related activities rather than district mandated activities is critical to success. Second, our initial model for the modules has changed significantly, in part due to observations during piloting and in part due to COVID-19. This has delayed publication of findings and materials.

Products:  We hope to have a full suite of field-tested modules available next year.

CAREER: Noticing and Using Students’ Prior Knowledge in Problem-Based Instruction (NSF #1253081)

PI:   Gloriana González Target Audience: High school geometry, high-need schools STEM Discipline: Mathematics

Products:  We developed 4 geometry problems situated in realistic contexts for high school students to use their prior knowledge.  Our publications show examples of ways in which they draw on their prior knowledge during problem-solving.  We also developed a set of animations with examples of how teachers  can use that prior knowledge during a problem-based lesson and guidance for engaging in a lesson study cycle using the problems as a source.  The project website has the problems, the animations, and a list of publications; a total of 13 journal publications (and 1 in-press), 2 book chapters (1 in press), and more than 20 presentations.   

COnceptual Model-based Math Intervention Tutor (COMMIT) (NSF #1503451)

PI:   Yan Ping Xin  | Co-PIs:  Yingjie Victor Chen, Signe Kastberg  Target Audience: Second, third, fourth grade students with learning difficulties in mathematics STEM Discipline: Mathematics

Current Findings Related to Problem Solving:  We conducted a RCT study involving 18 students with LDM to investigate the effects of the web-based computer tutor, COMMIT, on enhancing mathematical word problem-solving performance of students with LDM. Findings from this study indicated that COMMIT, which emphasizes mathematical model-based problem-solving, boosted participants’ performance above and beyond the business-as-usual (BAU) group. That is, although both groups improved their performance from pre- to posttest, the improvement rate of the students in the COMMIT group (effect size [ES] COMPS = 1.88) is much larger than that of the students in the BAU group (ES BAU = 0.98). Using gain scores as the measure, the ES between the two groups was 1.32 favoring the COMPS group. Additionally, the COMMIT group also took the Mathematics Problem Solving subset of the Stanford Achievement Test (SAT-10; Harcourt Assessment, 2004) before and after the COMMIT intervention. Results on SAT scores indicate that five out of nine (56%) participants in the COMPS-A group improved their SAT percentile rank after the intervention.

Key Challenge:  One challenge in conducting this type of work is the interdisciplinary nature of the work. The development of educational technologies requires the expertise of a diverse group of scholars who draw from different views of teaching and learning. Creating effective interfaces for learners and teachers demands that project team members develop a negotiated view of teaching and learning that can be drawn upon in the design of instructional activities, the programming of tools, and the implementation of the educational technology. Our engagement in conversations of views of teaching and learning overtime eventually result in a shared view.

Products:   Project Website | 2020 STEM for All Video Showcase Entry

National/Internationally Refereed Journal Articles

• Xin, Y. P., Kim, S. J., Lei, Q., Wei, S., Liu, B., Wang, W., Kastberg, S., Chen, Y., Yang, X., Ma, X., Richardson, S. E. (2020). The impact of a conceptual model-based intervention program on math problem-solving performance of at-risk English learners. Reading and Writing Quarterly, 36 (2), 104-123. https://www.tandfonline.com/doi/full/10.1080/10573569.2019.1702909
• Xin, Y. P., Park, J., Tzur, R., & Si, L. (2020). The impact of a conceptual model-based mathematics computer tutor on multiplicative reasoning and problem-solving of students with learning disabilities. The Journal of Mathematical Behavior, 58. available online Feb 27, 2020. https://doi.org/10.1016/j.jmathb.2020.100762
• Xin, Y. P. (2019). The effect of a conceptual model-based approach on “additive” word problem solving of elementary students who are struggling in mathematics.   ZDM : Mathematics Education, 51 (1), 139-150. DOI: 10.1007/s11858-018-1002-9
• Xin, Y. P.,Chiu, M. M., Tzur, R.  Ma, X., Park, J., Yang, X. (2019). Discourse-oriented instruction: How does a teacher’s talk affect math problem solving and reasoning of students with Learning disabilities. Learning Disability Quarterly, 43 , 43-56 doi.org/10.1177/0731948719858707 ".
• Xin, Y. P., Tzur, Si, L. Hord, C., Liu, J., Park, J. Y. (2017). An intelligent tutor-assisted math problem-solving intervention program for students with learning difficulties. Learning Disability Quarterly, 40(1), 4-16 .
• Xin, Y. P., Liu, J., Jones, S., Tzur, R., SI, L. (2016). A preliminary discourse analysis of constructivist-oriented math instruction for a student with learning disabilities. The Journal of Educational Research, 109 (4), 436-447. DOI:10.1080/00220671.2014.979910
• Xin, Y. P. & Hord, C. (2013). Conceptual model-based teaching to facilitate geometry learning of students who struggle in mathematics.  Journal of Scholastic Inquiry: Education, 1 (1), 147-160.
• Hord, C. & Xin, Y. P. (2013). Intervention research for helping elementary school students with math learning difficulties understand and solve word problems: 1996-2010.   Learning Disabilities: A Multidisciplinary Journal, 19 (1), 3-17.
• Zhang, D, Xin, Y. P., & Si, L. (2013). Transition from intuitive to advanced strategies in multiplicative reasoning for students with math difficulties.  The Journal of Special Education, 47 (1), 50-64.
• Xin, Y. P., Si, L., Hord, C., Zhang, D., Cetintas, S., & Park. J. Y. (2012). The effects of computer-assisted instruction in teaching Conceptual Model-Based problem solving.  Learning Disabilities: A Multidisciplinary Journal, 18 (2), 71-85. 
• Xin, Y. P., Zhang, D., Park, J. Y., Tom, K., Whipple, A., & Si, L. (2011). A comparison of two mathematics problem-solving strategies: Facilitate algebra-readiness.  The Journal of Educational Research, 104 , 381-395.
• Cetintas, S, Si,  L., Xin, Y. P., Zhang, D., Park, J. Y. & Tzur, R. (2010). A joint probabilistic classification model of relevant and irrelevant sentences in mathematical word problems.  Journal of Educational Data Mining, 2 (1), 83-101. 
• Cetintas, S., Si, L., Xin, Y. P., and Hord, C. (2010). Automatic detection of off-task behaviors in intelligent tutoring systems with machine learning techniques.  IEEE Transactions on Learning Technologies, 3 (3), 228-236.
• Xin, Y. P. & Zhang, D. (2009). Exploring a conceptual model-based approach to teaching situated word problems.   The Journal of Educational Research, 102 (6), 427-441.
• Xin, Y. P. (2008). The effect of schema-based instruction in solving word problems: An emphasis on pre-algebraic conceptualization of multiplicative relations.  Journal for Research in Mathematics Education, 39 , 526-551.
• Xin, Y. P., Wiles, B., & Lin, Y. (2008). Teaching conceptual model-based word-problem story grammar to enhance mathematics problem solving.  The Journal of Special Education, 42 , 163-178.

Peer-Reviewed Proceedings

• Kim, S., Kastberg, S. Xin, Y. P. Chen Y. & Wei, S. (2018).  Development of the composite unit in additive problem solving of student with mathematics difficulty in a computer based-learning environment. In Hodges, T.E., Roy, G. J., & Tyminski, A. M. (Eds.). (2018). Proceedings of the 40th annual meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education ( p.1280 ). Greenville, SC: University of South Carolina & Clemson University.
• Lei, Q., Xin, Y., P., Morita-Mullaney, T., & Tzur, R. (2018). Analyzing a discourse of scaffolds for mathematics instruction for an ELL with learning disabilities. In Hodges, T.E., Roy, G. J., & Tyminski, A. M. (Eds.). (2018). Proceedings of the 40th annual meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education (pp. 460-467). Greenville, SC: University of South Carolina & Clemson University.
• Wei, S., Xin,Y.P., & Chen,Y. (2020). Visualizing Students' Eye Movement to Understand Their Problem-Solving Process". Accepted by the Proceedings of the 22nd International Conference on Human Computer Interaction,  Copenhagen, Denmark.
• Wei, S., Lei, Q., Chen, Y., Xin, Y. P., Kastberg, S., & Kim, S. (2018). Evaluating the effects of highlighting text animations on the attention distribution of students with math learning difficulties.  Proceedings of the 2018 American Society for Engineering Education (ASEE) Conference.
• Wei, S., Chen, Y., Xin, Y. P., Kastberg, S. (2018). An exploratory approach to analyzing students’ eye movements when solving math problems. In Hodges, T.E., Roy, G. J., & Tyminski, A. M. (Eds.). (2018). Proceedings of the 40th annual meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education (pp. 1259-1262). Greenville, SC: University of South Carolina & Clemson University.
• Xin, Y. P., Kim; S., Kastberg, S., Chen, Y., Liu, B., Lei, Q., Wang, W., Richardson, S. E., Wei, S. (2018). The effect of a computer-assisted model-based problem-solving program for students with learning difficulties in mathematics. In Hodges, T.E., Roy, G. J., & Tyminski, A. M. (Eds.). (2018). Proceedings of the 40th annual meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education ( pp.1267-1270 ). Greenville, SC: University of South Carolina & Clemson University.
• Xin, Y. P., Kastberg, Si., & V. Chen. (2017). Conceptual Model-based Problem Solving: A response to intervention program for students with LDM. In E. Galindo & J. Newton (Eds.) Proceedings of the 39th annual meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education . Indianapolis, IN: Hoosier Association of Mathematics Teacher Educators, pp. 326.
• Xin, Y. P., Yang, X., Tzur, R. Park J., Ma. X (2016). PGBM-COMPS math problem-solving program: promote independent problem solving of students with LD.  Proceedings of 13th International Congress on Mathematical Education (ICME), Hamburg Germany.

DEAP: Developing and Evaluating Assessments of Problem Solving (NSF #s 1720646 , 1720661 )

PIs:   Jonathan Bostic , Toni Sondergeld  | Co-PI:  Gabriel Matney Target Audience:  Researchers interested in assessment development and use in STEM contexts; grades 3-8 mathematics teachers and school administrators STEM Discipline:  Mathematics

Theoretical Framework:  All PSM items are problems for students to solve, rather than exercises. For this project, problems are characterized as tasks meeting the following criteria: (a) it is unknown whether a solution exists, (b) a solution pathway is not readily determined, and (c) there exists more than one way to answer the task (Schoenfeld, 2011). PSM tasks are word problems, thus we draw upon a framework from Verschaffel and colleagues (1999) such that the word problems are (a) open, (b) developmentally complex, and (c) realistic tasks for an individual. Open tasks can be solved using multiple developmentally-appropriate strategies. Complex tasks are not readily solvable by a respondent and require productive thinking. Realistic tasks may draw upon real-life experiences, experiential knowledge, and/or believable events. Problems are distinct from exercises, whereas exercises are tasks intended to promote efficiency with a known procedure (Kilpatrick et al., 2001). Further, problem solving goes beyond the type of thinking needed to solve exercises (Mayer & Wittrock, 2006) and occurs when the task is a problem, not an exercise (Schoenfeld, 2011).

Methodology:  Once initial items are developed for PSMs, a systematic process is undertaken to collect five types of validity evidence, as suggested in The Standards for Educational and Psychological Testing (AERA et al., 2014): test content, response process, internal structure, relationship to other variables, and consequential. These sources of validity evidence inform continued or discontinued use of an item and whether additional items need to be developed. Test content validity evidence is collected through subject matter experts, including mathematics teachers, mathematics educators, and mathematicians, evaluating item-to-construct alignment through qualitative (logical or empirical) methods (Sireci & Faulkner-Bond, 2014). One-on-one and whole-class think alouds (Bostic et al., 2020) with typical student participants are used to inform response process validity evidence to ensure participant responses align with the test construct (Padilla & Benitez, 2014). For DEAP, internal structure validity evidence is assessed psychometrically using Rasch (1960) methods to explore instrument dimensionality, measurement invariance, and instrument reliability (Rios & Wells, 2014). Relationship to other variables validity evidence uses statistical testing to determine if PSM outcomes are associated with hypothesized variables (Beckman et al., 2005). Finally, consequential validity evidence (or bias) is examined through interviews to see how participants perceived the assessment to have impacted them (Bostic & Sondergeld, 2015) and through Rasch differential item functioning (DIF) analyses. Taken collectively, this evidence fuels the validity argument, which communicates how PSM users know score interpretations are appropriately grounded in sound claims and validity evidence.

Current Findings Related to Problem Solving:  Initial findings show that our team has successfully developed three problem-solving measures (PSM 3-5) that assess students’ abilities to solve CCSSM grade-level aligned problems. Each assessment has been explored rigorously through multiple validation studies to produce meaningful measures and score interpretations for researchers and schools. To date, anchor items used for linking assessments to each other have been initially tested and overall demonstrate acceptable for continued use. In our final year, we will test the vertical equating linking to ensure our operationalized construct of problem-solving ability from grades 3 to 8 has been appropriately measured in the anticipated manner.

Key Challenge:  Rigorous assessment development and validation is time intensive as it takes multiple cycles of creating and revising. Additionally, this work takes expertise from many different domains – subject matter experts, psychometricians, and school partners. It has taken our team many years of working together with diverse districts to construct a suitable test development process to conduct this type of work effectively and efficiently.

Products:  

• Bostic, J., Matney, G., Sondergeld, T., & Stone, G. (2020, April). Validation as design-based research: Implications for practice and theory. Paper presented at annual meeting of the annual meeting of the American Education Research Association. San Francisco, CA.
• Bostic, J., Matney, G., Sondergeld, T., & Stone, G. (2020, April). Developing a series of problem-solving measures for elementary students. Paper presented at annual meeting of the annual meeting of the American Education Research Association. San Francisco, CA.
• Bostic, J., Matney, G., Sondergeld, T., & Stone, G. (2020, March). Measuring what we intend: A validation argument for the grade 5 problem-solving measure (PSM5). Validation: A Burgeoning Methodology for Mathematics Education Scholarship . In J. Cribbs & H. Marchionda (Eds.), Proceedings of the 47th Annual Meeting of the Research Council on Mathematics Learning (pp. 59-66). Las Vegas, NV.
• Bostic, J., Matney, G., Sondergeld, T., & Stone, G.  (2019, July). Developing a problem-solving measure for grade 4. In M. Graven, H. Venkat, A. Essien, & P. Vale (Eds.), Proceedings of the 43 rd Meeting of the International Group for the Psychology of Mathematics Education (Vol. 4, p 4-104). Pretoria, South Africa. https://www.up.ac.za/media/shared/705/pme43/Proceedings/PME43Volume-4-proceedings-oc_pp.pdf
• Bostic, J.(2019, October). We can do better! Intersection Points, 44 (6), p. 3-4.
• Bostic, J., Matney, G., Sondergeld, T., & Stone, G. (2019, February). Validation: A Burgeoning Methodology for Mathematics Education Scholarship . In A. Sanogo & J. Cribbs (Eds.), Proceedings of the 46th Annual Meeting of the Research Council on Mathematics Learning (pp. 43-50). Charlotte, NC.
• Bostic, J.,Matney, G., Sondergeld, T., & Stone, G. (2018, November). Content validity evidence for new problem-solving measures (PSM3, PSM4, and PSM5). In T. Hodges, G. Roy, & A. Tyminski (Eds.), Proceedings for the 40 h Annual Meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education (pp. 1641). Greenville, SC.
• Sondergeld, T. A. (2020). Shifting sights on STEM education instrumentation development: The importance of moving validity evidence to the forefront rather than a footnote. School Science and Mathematics Journal. https://onlinelibrary.wiley.com/doi/abs/10.1111/ssm.12410?af=R
• Sondergeld, T., Stone, G., Kruse, L., Bostic, J., & Matney, G. (2020, April). Evaluating Dichotomous and Partial-Credit Scoring within a Constructed-Response Assessment: Is More Information Always Psychometrically Better? Paper presented at annual meeting of the annual meeting of the American Education Research Association. San Francisco, CA.
• Sondergeld, T., Stone, G., Bostic, J., & Matney, G., (2019, July). Validity in a different context: Exploring relations to other variables evidence. In M. Graven, H. Venkat, A. Essien, & P. Vale (Eds.), Proceedings of the 43 rd Meeting of the International Group for the Psychology of Mathematics Education (Vol. 4, p. 4-104). Pretoria, South Africa. https://www.up.ac.za/media/shared/705/pme43/Proceedings/PME43Volume-4-proceedings-oc_pp.pdf
• Stone, G. E., Sondergeld, T. A., Bostic, J., & Matney, G. (2019, July). Validity in a different context: Exploring relationship to other variables validity evidence. In M. Graven, H. Venkat, A. Essien, & P. Vale (Eds.), Proceedings of the 43 rd Meeting of the International Group for the Psychology of Mathematics Education (Vol. 4, p. 4-104). Pretoria, South Africa. https://www.up.ac.za/media/shared/705/pme43/Proceedings/PME43Volume-4-proceedings-oc_pp.pdf

Ed+gineering: An Interdisciplinary Partnership Integrating Engineering into Elementary Teacher Preparation Programs (NSF #1908743)

PI:   Jennifer Kidd  | Co-PIs:  Kristie Gutierrez, Krishnanand Kaipa, Pilar Pazos-Lago, Stacie Ringleb Target Audience:  Our project partners undergraduate education and engineering students to teach engineering lessons to 4th and 5th graders. STEM Discipline:  Engineering

Ed+gineering’s goal is to increase PSTs’ engineering confidence and engineering students’ ability to communicate effectively in interdisciplinary contexts. Teaching engineering lessons in a supported context helps increase PSTs’ self-efficacy and intention to teach engineering. Engineering students who seek out and value the contributions of non-technical peers are well positioned to address complex global challenges.

Products:   Coming Soon! Project Website

Project Videos

• 2020 STEM for All Video Showcase Entry
• Teaching robotics while quarantined: A profile of one team’s journey (Ed+gineeringODU Spring 2020)
• ODU National Active Learning Day
• Engineering Lessons Project 2016 @ ODU
• WOW Club 2018

ASEE 2020 Conference Presentations

• What do Undergraduate Engineering Students and Pre-service Teachers Learn by Collaborating and Teaching Engineering and Coding Through Robotics?
• The First Year of an Undergraduate Service Learning Partnership to Enhance Engineering Education and Elementary Pre-Service Teacher Education
• Enhancing Teamwork Skills Through an Engineering Service-learning Collaboration
• Partnering Undergraduate Engineering Students with Preservice Teachers to Design and Teach an Elementary Engineering Lesson Through Ed+gineering    

FLECKS: Fostering Collaborative Computer Science Learning with Intelligent Virtual Companions for Upper Elementary Students (NSF #s 1721000 , 1721160 )

PIs:   Kristy Boyer ,  Eric Wiebe  | Co-PI:  Collin Lynch  Target Audience: Upper elementary (Grades 4-5) students, especially populations that historically have been underrepresented in Computer Science STEM Discipline:  Computer Science

Theoretical Framework:  The theoretical work of Mercer and colleagues (1999; 2002; 2004) has informed both our analysis of collaborative work and the goals for more productive collaboration. In addition, we are also interested in how dyads of students regulate their collaboration. This theoretical lens is informed by the work of noted scholars in the field (Hadwin, Wozney, & Pontin, 2005; Janssen, Erkens, Kirschner, & Kanselaar, 2010; and Kumpulainen & Mutanen, 1999).

Methodology:  The primary approach to analyzing student discourse has been qualitative analysis of video and audio recordings of student work, guided by rubrics designed through the lens of the above-mentioned theoretical frameworks. In addition, we make extensive use of participatory design techniques in the development of our prototype learning environments. More recently, we have also been utilizing Epistemic Network Analysis (ENA; Shaffer, Collier & Ruis, 2016) to further unpack students’ discursive practices.

Current Findings Related to Problem Solving:  Productive collaborative problem solving does not just happen, especially with younger students. Efficacious practices have to be modeled and supported by teachers and intelligent systems. We have learned that interpersonal dynamics will often lead students away from productive problem-solving and into unproductive conflict if not properly supported. Similarly, students need to be taught how to employ questioning techniques that move beyond low level questioning, where they are spurring deeper thinking on the part of their partner as they explain and defend their strategies and solutions.

Key Challenge:  One of our challenges is the development of activities and design of a learning environment that serves and supports a diverse audience of students. In addition, elementary teachers have historically not taught computer science or collaborative problem solving in technology-rich environments. We have had to devote effort to properly supporting teachers and seeking feedback from a broad range of students through our design and development process.

• Tsan, J., Vandenberg, J., Fu, X., Wilkinson, J., Boulden, D., Wiebe, E., Lynch, C., & Boyer, K. E. (June, 2019). Conflicts and collaboration: A study of upper elementary students solving computer science problems. International Conference on Computer Supported Collaborative Learning (CSCL 2019) . Lyon, France.
• Tsan, J., Vandenberg, J., Fu, X., Wilkinson, J., Boulden, D., Boyer, K. E., ... & Wiebe, E. (February, 2019). An investigation of conflicts between upper-elementary pair programmers. Proceedings of the 50th ACM Technical Symposium on Computer Science Education (pp. 1264-1264). ACM.
• Tsan, J., Vandenberg, J., Zakaria, Z., Wiggins, J. B., Webber, A. R., Bradbury, A., Lynch, C., Wiebe, E., Boyer, K. E., (2020, March) A comparison of two pair programming configurations for upper elementary students. Proceedings of the 51st ACM Technical Symposium on Computer Science Education (SIGCSE ’20) , Portland, OR (pp. 346-352).
• Vandenberg, J., Tsan, J., Boulden, D. C., Zakaria, Z., Lynch, C., Boyer, K. E., & Wiebe, E. (2020). Elementary students' understanding of CS terms. ACM Transactions on Computing Education (TOCE), 20 (3). https://dl.acm.org/doi/pdf/10.1145/3386364
• Vandenberg, J., Tsan, J., Zakaria, Z., Boulden, D. C., Boyer, K. E., Lynch, C., & Wiebe, E. N. (2020, April). Elementary learners’ regulation in computer-supported collaborative learning environments . Paper accepted for presentation at the annual meeting of the American Educational Research Association (AERA), San Francisco, CA.
• Wiggins, J., Wilkinson, J., Lynch, C., Wiebe, E., & Boyer, K. E. (June, 2019). From doodles to designs: Participatory edagogical agent design with elementary students. Proceedings of the 18th ACM International Conference on Interaction Design and Children (IDC 2019) (pp. 642-647). ACM. doi: 10.1145/3311927.3325321
• Zakaria. Z., Boulden. D. C., Vandenberg, J., Tsan, J., Lynch, C. Boyer, K. E. & Wiebe, E. N. (April, 2019). Elementary students’ collaborative practices in side-by-side programming . AERA Annual Meeting, Toronto, CA.
• Zakaria, Z., Vandenberg, J., Boulden, D. C., Tsan, J., Boyer, K. E., Lynch, C., & Wiebe, E. N. (2020, April). Feedback to improve collaboration in pair programming . Paper accepted for presentation at the annual meeting of the American Educational Research Association (AERA), San Francisco, CA.
• Zakaria, Z., Wiebe, E., Boulden, D., Tsan, J., Vandenberg, J., Lynch, C. & Boyer, K. (June, 2019). Collaborative talk across two pair-programming configurations. International Conference on Computer Supported Collaborative Learning (CSCL 2019) . Lyon, France.

Improving Grades 6-8 Students' Mathematics Achievement in Modeling and Problem Solving through Effective Sequencing of Instructional Practices (NSF #1907840)

PI:   Joe Champion |  Co-PIs:   Michele Carney , Samuel Coskey , Ya Mo, Keith Thiede Target Audience: Grades 6-8 students STEM Discipline: Mathematics

Theoretical Framework:  ROOT aims to adopt and extend features of effective mathematics instruction as outlined by Hiebert and Grouws (2007). The Explicit Attention to Concepts (EAC) and Students’ Opportunities to Struggle (SOS) conceptual framework was successfully used by Stein and colleagues (2017) for large-scale classroom teacher research, and our project has been heavily invested in extending Stein’s work through a combination of video analysis, surveys, and student achievement data.

Key Challenge:  ROOT addresses the key challenge of conducting classroom intervention research that both offers support and choice for teachers to innovate in their local context and generates transferable research evidence about how instructional strategies can improve student learning.

Product: Project Website | PD Modules One , Two , Three | EAC & SOS Guide

Mathematical and Computational Methods for Planning a Sustainable Future (PS-Future) (NSF #1503414)

PI:   Margaret Cozzens  | Co-PIs:   Tamra Carpenter , Rebecca Jordan Target Audience: High school STEM Disciplines:  Mathematics, Environmental Science, Biology, Computing, Social Sciences

Project research and evaluation studies investigated and validated our belief that short (roughly one week) modules can positively impact students’: 1) engagement in mathematics and computing by immersion in sustainability topics of personal relevance; 2) confidence in using cross-cutting skills in mathematical and computational methods for scientific inquiry; and 3) learning of the STEM practices and modes of inquiry necessary to become the next generation of interdisciplinary problem solvers.

Theoretical Framework & Methodology:  Given the increased demand for inter- and cross-disciplinary thinking, especially in environmental and sustainability related fields, special attention needs to be given to the means by which these complex environmental problems are understood. We feel that thinking within complex systems is essential for learners to grasp the non-linear and dynamic nature of real world problems and have focused our PS Futures learning modules on the quantitative reasoning and operations associated with sustainability related problems. Our work is nested in theory that suggests that learning is mediated by the tools and outcomes in an activity system surrounding the learners and that learners need to collaboratively attend to abstractions that can help encourage learning transfer (e.g., Palincsar 1998; Bransford et al. 1999). The tools and outcomes in our modules integrate social and scientific concepts embedded in mathematical operations that students encounter in their math and environmental science courses. We use mental models made visible through a specific conceptual representation that helps the learners to tie together the multiple ideas encountered in the modules. Because models often include a small number of semantic representations, individuals coming from different backgrounds, classrooms, and engaging with different task structures, once familiar with model terms, can communicate their ideas in a standardized space that can be compared across contexts.

Current Findings Related to Problem Solving:  We were able to code data from 84 pre-module and 147 post-module models in which learners were asked to tie together ideas and transfer knowledge to a novel context. In this way, these learners can describe on a system level the mathematical strategies necessary to understand the environmental problem posed. From these models a number of trends emerged. First, and not surprisingly, the models were more accurate following instruction. Second, the post-module models tended to represent more causal agents, which suggests an increased understanding of how specific phenomena are driven by underlying mechanisms versus specific parts. In this way, we argue that as learners encounter novel spaces where similar thinking strategies are warranted, they are more likely to implement their more accurate mathematical practices. To support this contention, we found that when students represented greater mechanism they tended to do so at a more abstract scale, which tend to be more applicable across environments. While at this point only a speculation, there is enough evidence to suggest deeper investigation would be enlightening.

Key Challenges:  As we were finishing publication of the modules, we discovered that the Cornell Ornithology Center was no longer supporting the “Yardmap” software they had developed, and by January 31, 2020, they had pulled it entirely from their website. One of the modules, called Life on the Edge, made extensive use of the software by students in designing suitable habitats. This section had to be rewritten and a new methodology found for developing and representing animal habitats. Fortunately, we were able to use Google Earth to accomplish all of tasks required. Google Earth is available to students through Google Classroom or independently. This work was completed in early June, completing the module development.

A typical challenge for this type of work is that of implementing a complex pre-post test project evaluation that involves models versus finite-scope types of questionnaires. That is to say, learners have the open space to model whatever aspect of the novel system, which can result in numerous possibilities that may reflect classroom-specific detail versus across-classroom ideas.

Doing this type of work, requires that the classroom instructors invest similar amounts of time and intellectual energy to the task which is not always feasible given the incredibly dynamic classroom space.

Products:   Instructional Modules | Project Website |  2018 STEM for All Video Showcase Entry  

Mathematical Learning via Architectural Design and Modeling Using E-Rebuild (NSF #1720533)

PI:   Fengfeng Ke  | Co-PIs:  Russell Almond, Kathleen Clark, Gordon Erlebacher, Valerie Shute Target Audience: Middle school students STEM Disciplines:  Mathematics

Theoretical Framework:  Realistic mathematics education, learning through making and design, constructionism, and learning with multiple representations.

Methodology:  The project uses a design-based research approach to gather data from students and teachers that will inform the design of the learning environment. The qualitative and quantitative data will also be used to understand what students are learning as they play the game and how teachers are interacting with their students.

Current Findings Related to Problem Solving:  The initial findings of a recent mixed-method infield study indicate a positive effect and an advantage of using E-Rebuild in the classroom, in comparison with a business-as-usual control group, on the math problem-solving test performance of middle school students. There was a significant improvement with a large effect size from pretest to posttest by the E-Rebuild participants in the math problem-solving test performance. Controlling for the pretest performance, the E-Rebuild group significantly outperformed the control group in the post-treatment math problem-solving test with a medium-large effect size. The qualitative behavior analysis of participants’ gameplay processes also indicated a trend of increased engagement in mathematical problem-solving actions and the development of mindfulness with positive attitudes toward mathematical reasoning and conception.

Products:   E-Rebuild Game, Tutorial, & Demos

Proportions Playground (NSF #1621290)

PI:   Chandra Orrill Target Audience: Middle school teachers STEM Disciplines:  Mathematics

Theoretical Framework:  Our work is grounded in two frameworks. The first is Knowledge in Pieces (from diSessa) which allows us a lens to think about how adults understand proportional relationships. While teachers may have a wide range of understandings, those understandings may not be interconnected in ways that support problem solving or the teachers may be missing key knowledge resources. The items we’ve developed attempt to push on making connections between independent knowledge resources, thus deepening teachers’ knowledge bases. 

Our other framework is playing with math. This means engaging in problem solving in a way that relies on making and testing conjectures and math arguments that can be reasoned about, tested, illustrated, and explained through the use of dynamic environments. This necessarily occurs in a safe space built on discussion driven by challenges, explorations, and mysteries that are perceived to be worthwhile to engage in and to discuss.  Worthwhile tasks are those that are relevant to classroom teaching, allow argument, often feature ambiguity (because that’s where argument can happen), and are open middle and/or open ended.

Methodology:  Because this is an exploratory project, we have been using traditional qualitative methods relying primarily on videotaping professional development sessions.

Current Findings Related to Problem Solving:  Two critical findings have emerged. First, the teachers love this approach. They rate the experiences very highly, report high engagement, and leave the PD smiling. Teachers want to be challenged, and this approach challenges them. Second, tasks need to be ambiguous to generate the playful exploration necessary to engage in this learning. This is very different from traditional views in which math problems are made as precise as possible. In our conversations, we explicitly discuss the need for precision by building from the perturbation that emerged from the ambiguity.

Products:   Proportions Playground Toys | Proportions Playground Tasks

Science and Engineering Education for Infrastructure Transformation (NSF #1721054)

Description:  Future sustainable and resilient infrastructure will be powered by renewable energy, be able to respond intelligently to changes in the environment, and support smart and connected communities. The transformation of our infrastructure calls for millions of problem solvers that our education system must cultivate. One way to fill the gap is to build cyberlearning infrastructures that enable students to learn how to solve those problems in the classroom with STEM. This project has developed two such programs, the Virtual Solar Grid and the IoT Workbench. These programs create rich problem-solving activities with sound pedagogies at the intersection of science, engineering, and computation and in the context of solar power engineering and cyber-physical systems. In the past year, we conducted research with 132 high school students and 52 middle school students with highly diverse socioeconomic backgrounds. The results show that students improve their science understanding while acquiring problem-solving abilities such as claim-evidence-reasoning, engineering design, and computational modeling. One of the teachers spoke highly of the solar energy project. “This is the first time of the year that all 16 pairs of eyes paid attention with invested attitudes,” referring to one of her classes that was relatively underperforming.

Products:   Virtual Solar Grid | IoT Workbench

Systemic Transformation of Inquiry Learning Environments for STEM (NSF #2010530)

PI:   Ellen Meier |  Co-PI:  Bryan Keller Target Audience: Grades 3-8, Urban Schools STEM Discipline:  Transdisciplinary STEM; STEM as a meta-discipline; cross-cutting concepts in STEM, which can involve other disciplines beyond science, technology, engineering, and mathematics.

With the NSF funding, the project team will study the utility and feasibility of the model in an effort to streamline the approach and to create broader access to high-quality professional learning. Over the course of the project, Grades 3-8 teachers from twenty-five schools in New York City and New Haven, CT will participate in a multi-year online workshop and coaching series to design and implement rigorous and culturally relevant learning experiences focused on cross-cutting concepts across the STEM disciplines. Complementary work with school-based administrators and instructional coaches will support leaders in strategically supporting the change process. 

Products: The systemic work leverages the Innovating Instruction approach to professional learning developed by the Center for Technology and School Change at Teachers College, Columbia University. A publically available version is available on the Center’s website .

Zoombinis: The Implementation Research Study of a Computational Thinking Game for Upper Elementary and Middle School Learners (NSF #1502882)

PI: Jodi Asbell-Clarke | Co-PIs: Teon Edwards , Elizabeth Rowe Target Audience:  National, Grades 3-8; Sub-study on students with IEPs or 504s for neurodiversity Discipline/Focus: Computational Thinking

Zoombinis engages learners in problem-solving practices such as Problem Decomposition, Pattern Recognition, Abstraction, and Algorithm Design. We provide “bridge activities” that helps teachers connect the implicit CT problem-solving practices in the game to explicit classroom problem-solving in other subjects such as science and math.

We studied how teachers use the learning game, Zoombinis , to solve problems using Computational Thinking (CT) practices. While students in grades 3-8 solve fun logic puzzles in the game, teachers connect those practices to problem-solving in STEM and everyday activities.

Theoretical Framework:  We draw from theory that considers CT as a specific form of problem-solving to study problem solving in the game Zoombinis . Our research goal was to examine how students build CT practices such as problem decomposition, pattern recognition, abstraction, and algorithm design within the context of the increasingly complex logic puzzles in the game.

We also use theory from game-based learning where students build implicit skills and knowledge through engaged problem-solving. This framework differentiates between explicit knowledge, what wecan express, from implicit knowing, which is what we are able to do . Implicit knowledge is, by definition, largely unexpressed by the learner yet is considered foundational to all knowledge. Explicit knowledge is what educators typically attempt to measure in learning assessments. Games present a rich opportunity to support and measure implicit learning because players are often immersed in problem-solving situations where they experiment with the mechanics to understand the rule system, using trial and error with helpful feedback and rewards for motivation and sustain engagement.

We built educational data mining models as a form of embedded implicit assessments, assessments that could measure CT practices using the game log data generated by students’ gameplay. One component of our research study was to examine the validity of these detectors as assessments of students’ CT. Another component of the research was to examine the relationship between students’ activity in the game, teachers’ activity in the class, and the outcomes of students’ CT practices. Our previous research has shown that teachers are key to bridging implicit game-based learning to explicit classroom learning, so we provided teachers with bridging activities and support while they implemented the game in their classes.

Methodolgy:  We used a combination of research methods, including educational data mining on game data logs, cluster analysis on teacher logs of classroom activity, and multilevel modeling, to determine the impact of the duration and nature of student gameplay, as well as the extent and nature of classroom activity, on student CT practices.

To develop and validate implicit learning assessments using Zoombinis log data, we designed automated detectors of CT practices within Zoombinis game play. We collected video and screencapture along with data logs from a wide variety of Zoombinis players. After extensive observations and human-labelling of the data, we build educational data mining (EDM) modes to detect those strategies automatically in student gameplay. These automated detectors provide real-time assessment of CT practices students build implicitly within the game.

To validate the detectors as measures of CT, we correlated the detectors with external measures of students’ CT practices. Most CT practice detectors were significantly correlated with these external measures.

To serve as external pre- and post-assessments of CT, the authors worked with a game-based learning company to design Interactive Assessments of CT (IACT)—a set of online, non-coding, non-textual logic puzzles to assess CT practices in upper elementary and middle school.

Current Findings Related to Problem Solving:  Automated detectors of gameplay CT practices built for this research were significant predictors of external post-assessment scores, and thus show promise as implicit assessments of CT practices within gameplay . 

Students with high duration of gameplay and high gameplay CT practices scored highest on external post-assessment of CT practices, when accounting for pre-assessment scores. This research suggests that Zoombinis is an effective CT learning tool and CT assessment tool for elementary- and middle-school students.

In a small sub-study of classes where we were able to collect students’ IEP status, we found that when teachers used a combination of Zoombinis and classroom bridging activities the outcomes of students with IEP status are comparable to their peers.

Key Challenge:  Our biggest challenge was the lack of available external assessments in CT that met the diverse needs of our target audience. We designed our own assessments because we could not find established instruments that did not rely on significant text or coding, which may have been a barrier for some students. In a larger study using an augmented sample, we found moderate evidence of concurrent validity and strong evidence of test-retest reliability for IACT, so we used these for outcome measures in our studies. The research findings would be stronger with a more psychometrically-validated external assessment instrument that also accessible to a broad audience.

Products: Zoombinis Game | Zoombinis Apps ( Apple Store, Google Play )

• Asbell-Clarke, J., Rowe, E., Almeda, V., Edwards, T., Bardar, E., Gasca, S., Baker, R.S., & Scruggs, R. (under review). The Development of Students’ Computational Thinking Practices in Elementary- and Middle-School Classes using the Learning Game,  Zoombinis.
• Asbell-Clarke, J. ,Rowe, E. , Almeda, V., Gasca, S. , Edwards, T. , Bardar, E. ,Shute, V. , and Ventura, M. (under review). Interactive Assessments of CT (IACT): Digital Interactive Logic Puzzles to Assess Computational Thinking in Grades 3–8.
• Asbell-Clarke, J., Rowe, E.   , Almeda, V., Edwards, T.   , Bardar, E. (under review). Bridging the Gap: Using the Computational Thinking Game,  Zoombinis , to Support Neurodiverse Learners.
• Rowe, E., Asbell-Clarke, J., & Almeda, M. Scruggs, R., Baker, R.S., Bardar,E. & Gasca, S. (under review) Assessing Implicit Computational Thinking in  Zoombinis  Puzzle Gameplay.  Submitted to a special issue of Computers & Human Behavior on Learning Analytics and Assessment.
• Almeda, M., Rowe, E., Asbell-Clarke, J., Baker, R., Scruggs, R., Bardar, E., & Gasca, S. (2019, October). Modeling Implicit Computational Thinking in Zoombinis Mudball Wall Gameplay.  Paper submitted to the Technology, Mind, and Society conference, October, Washington D.C.
• Rowe, E., Asbell-Clarke, J., Almeda, M., Bardar, E., Baker, R. S., & Scruggs, R., (2019). Advancing Research in Game-Based Learning Assessment: Tools and Methods for Measuring Implicit Learning.  In E. Kennedy & J. Qian (Eds.) Advancing Educational Research with Emerging Technology. IGI Global.
• Rowe, Rowe, E., Asbell-Clarke, J., & Baker, R. (2019, April). Game-based measures of implicit learning. Structured poster session organized by Y.J. Kim titled   Game-Based Assessment: How Has the Field Matured over the Past 10 years?  AERA Annual Meeting, Toronto.
• Rowe, E., Asbell-Clarke, J., Baker, R., Gasca, S., Bardar, E., & Scruggs, R. (2018, April). Labeling Implicit Computational Thinking in Pizza Pass Gameplay.  Late-breaking work presented at the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI 18), Montreal. https://doi.org/10.1145/3170427.3188541
• Rowe, E., Asbell-Clarke, J., Baker, R., Gasca, S., Bardar, E., & Scruggs, R. (2017, April). Labeling Implicit Computational Thinking in Pizza Pass Gameplay.  Late-breaking work presented at the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI 18), Montreal.  https://doi.org/10.1145/3170427.3188541
• Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking . Educational Research Review, 22, 142-158.
• Rowe, E., Asbell-Clarke, J., Cunningham, K. & Gasca, S. (2017, October). Assessing implicit computational thinking in Zoombinis gameplay: Pizza Pass, Fleens, and Bubblewonder Abyss. Work-in-progress presented at the ACM SIGCHI Annual Symposium on Computer-Human Interaction in Play, Amsterdam.
• Rowe, E., Asbell-Clarke, J., Gasca, S., & Baker, R. (2017, October). Computational thinking in Zoombinis gameplay. Spotlight session at the 8th Digital Media & Learning Conference in Irvine, CA.
• Rowe, E., Asbell-Clarke, J., Gasca, S., & Cunningham, K. (2017, August). Assessing implicit computational thinking in Zoombinis gameplay. Poster presented at the International Conference on the Foundations of Digital Games in Hyannis, MA.
• Rowe, E., Asbell-Clarke, J., Gasca, S., & Baker, R. (2017, April). Computational Thinking in Zoombinis Gameplay. Poster presented at the Cyberlearning Conference in Arlington, VA.
• Algebra Education
• Argumentation, Critique, & Other Discursive STEM Practices
• Computer Science & Computational Thinking
• Broadening Youth Participation in Computer Science & Engineering
• Collaborative Learning
• Computational Thinking  

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Innovative approaches to STEM teaching: Preparing the next generation

The importance of STEM education—science, technology, engineering, and mathematics—cannot be overstated. STEM workers support innovation and propel technological advancement, which is important for our country’s standards of living, economic growth, and positioning on the global stage. 1 As emerging technologies continue to reshape industries, there is a growing need for STEM educators who can equip students with the skills they need to thrive in the 21st century.

This blog post explores innovative approaches to STEM teaching, examining how these methods not only engage students but also prepare them to tackle real-world challenges. From pedagogy to classroom setup to technology integrations, we will explore a range of strategies that are transforming modern STEM classrooms.

21st-century STEM education

Modern STEM education is focused on developing a set of essential skills to set students up for future success and lifelong learning in an increasingly complex world. These competencies include: 2

• Digital/computer literacy: This type of literacy ensures that students are adept at using modern technologies and can navigate the digital world confidently
• Critical thinking and problem-solving: These skills help students analyze complex issues and devise effective solutions 
• Collaboration: This teaches students how to work efficiently in teams, an invaluable skill in today's interconnected and interdisciplinary workforce

Global competence

Preparing students for a globalized workforce means equipping them with the cultural awareness and communication skills necessary to collaborate with peers from diverse backgrounds. STEM educators can foster global competence by incorporating education focused on social stewardship and policy implication, rather than just focusing on technical topics. 3 Include international perspectives into your curriculum by facilitating cross-cultural projects and promoting language skills. Understanding global issues, such as climate change and sustainable development, from multiple cultural viewpoints helps students appreciate the broader impact of their work and encourages innovative, inclusive solutions.

Lifelong learning

In an era marked by rapid technological advancements and continual innovation, the ability to adapt and learn new skills is more important than ever. STEM education should not only provide foundational knowledge but also instill a mindset of continuous growth and curiosity. 4 Educators can encourage lifelong learning by promoting self-directed projects, offering opportunities for professional development, and integrating flexible learning pathways into the curriculum. This approach prepares your students to remain agile, resilient, and capable of navigating the evolving demands of the STEM landscape throughout their careers.

STEM pedagogy

As an educator, you have a variety of pedagogies—the methods in which you teach—available to you. If you are currently a teacher, you may have a preferred pedagogy that works for your classroom. However, consider the following pedagogical approaches for STEM education.

Constructivist approaches

A constructivist pedagogy emphasizes the importance of students building their own knowledge and understanding through exploration and discovery. 5 Rather than simply absorbing information, students actively construct knowledge by engaging in hands-on activities, experiments, and problem-solving tasks. This learner-centered approach fosters a deeper comprehension of concepts and encourages students to develop critical thinking and independent learning skills.

Differentiated instruction

Differentiated instruction addresses students’ varied learning needs by tailoring teaching techniques and materials to accommodate different learning styles, abilities, and interests. 6 By offering a range of instructional strategies, such as tiered tasks, flexible grouping, and scaffolded support, you can help all of your students, regardless of their individual differences, succeed in STEM subjects.

Reflective teaching practices

Reflective teaching asks you to examine the effectiveness of your teaching practices. 7 By regularly assessing and analyzing your instructional methods, you can identify areas for growth and adapt your strategies to better meet student needs. Reflective practices may involve self-evaluation, peer observations, and student feedback. This ongoing process of reflection and adaptation helps create an evolving and effective STEM learning environment.

Project-based learning in STEM: Enhancing student engagement

Project-based learning (PBL) is an instructional method that involves students exploring real-world problems and challenges through in-depth projects. 8 It is particularly effective in STEM education because it promotes hands-on learning, critical thinking, and the application of technical skills. Unlike traditional models of instruction, where knowledge is often siloed and theoretical, PBL integrates multiple disciplines and focuses on practical, tangible outcomes.

The benefits of PBL in STEM include increased student engagement, improved understanding of complex concepts, and the development of essential life skills such as time management, teamwork, and communication. 9

Effectively implementing Project-Based Learning (PBL) in STEM classrooms requires careful planning, structured yet flexible frameworks, and ongoing support for both students and educators. Strategies for successful PBL implementation include: 10

• Starting with a driving question: Formulate a compelling question that the project will address
• Facilitating inquiry and research: Provide students with the resources and guidance needed to conduct thorough research
• Creating a timeline: Break the project into manageable phases and set clear milestones. Regular check-ins can help ensure that students are on track and provide opportunities for feedback and adjustment
• Encouraging collaboration: Promote teamwork by forming diverse groups where students can leverage each other’s strengths
• Integrating reflection: Include opportunities for students to reflect on their learning process, challenges faced, and skills acquired

Additional STEM teaching methods

In addition to project-based learning, you can teach your students using a flipped classroom model or a design thinking methodology.

The flipped classroom model is revolutionizing STEM education by shifting the traditional teaching paradigm. In this model, students are first exposed to new content outside of class, typically via online lectures or reading assignments. 11 Class time is then utilized for interactive activities that reinforce and apply the learned concepts. This approach promotes deeper understanding and allows for personalized instruction, making complex STEM subjects more approachable. This model can be applied to students of all ages, but high school students tend to benefit most from this approach, as it prepares them well for college and the workforce. 11

Design thinking originates from the world of design and emphasizes empathy, ideation, prototyping, and testing within the framework of problem-solving. 12 In STEM projects, design thinking encourages students to approach challenges from multiple perspectives, empathize with end users, and iterate on their solutions based on feedback. This cultivates a mindset of continuous improvement and adaptability, crucial traits for success in the fast-evolving STEM fields.

Modern STEM classrooms

The physical layout of your classroom plays an important role in how students will respond to the teaching methods we’ve discussed. Consider the following suggestions for your STEM classroom.

Flexible learning spaces

The design of modern STEM classrooms is increasingly focused on promoting collaboration and active learning. Flexible learning spaces, equipped with movable furniture and multifunctional areas, allow students to work in groups or individually as needed. 13 These adaptable environments support various teaching methods, from lectures to project-based learning, and encourage student interaction, creativity, and engagement.

Technology-enhanced learning

The integration of advanced technologies is a hallmark of modern STEM classrooms. Tools such as interactive whiteboards, 3D printers, virtual labs, and augmented reality (AR) applications bring abstract concepts to life and provide immersive learning experiences. 13 These technologies not only facilitate a deeper understanding of the material but also prepare students for the tech-driven world they will encounter in their future careers.

Technology integration in STEM education: Tools and techniques

Technology plays a crucial role in enhancing STEM education by providing students with interactive and immersive learning experiences. Here are some essential tools commonly used in STEM classrooms:

• Coding platforms: Tools like Scratch, Code.org, and Python teach students programming through engaging activities 14
• Robotics kits: Kits such as LEGO Mindstorms, VEX Robotics, and Arduino provide hands-on experience in building and programming robots, as well as teaching fundamental principles of engineering, mechanics, and computer science 15
• 3D printing: 3D printers give students the ability to bring their designs to life, fostering creativity and innovation and helping students understand the practicalities of product development 15

Future trends

As technology continues to evolve, new trends are emerging that have the potential to further transform STEM education. Here are some of the future trends to watch:

• Artificial intelligence (AI) and machine learning (ML): AI and ML are increasingly being integrated into educational tools to provide personalized learning experiences. These technologies can analyze student performance and adapt instruction to meet individual learning needs, making education more efficient and effective 16
• Virtual and augmented reality (VR/AR): VR and AR technologies offer immersive learning experiences that can bring complex STEM concepts to life. For example, students can explore virtual laboratories, conduct simulations of chemical reactions, or visualize anatomical structures in 3D 15
• Internet of Things (IoT): The IoT connects physical devices to the internet, enabling new forms of data collection and analysis. In STEM education, IoT devices can be used for hands-on experiments, such as monitoring environmental conditions or creating smart devices 17
• Adaptive learning technologies: Adaptive learning systems tailor educational content to meet the unique needs of each student. These technologies use data analytics to identify areas where students are struggling and provide targeted resources and interventions 18  

These technologies will enhance STEM education, making it more interactive, personalized, and effective.

Assessment strategies in STEM education: Measuring success

Continuous curriculum innovation is an important aspect of STEM education. Employing effective assessment strategies is one way that educators can ensure their teaching practices and curricula remain relevant and effective.

Let’s explore the various types of assessments you can apply to your courses.

Formative and summative assessment

Both formative and summative assessments play crucial roles in measuring student success in STEM education. Formative assessments are ongoing evaluations that provide real-time feedback to students and instructors, helping to identify areas where students may need additional support. 19 These can include quizzes, in-class activities, and peer reviews. The primary goal of formative assessments is to monitor student learning and make timely adjustments to teaching strategies.

Summative assessments are typically administered at the end of a unit or course to evaluate overall student learning. 19 Examples include final exams, standardized tests, and end-of-term projects. Summative assessments provide a comprehensive overview of what students have learned and how well they have mastered the course objectives.

Authentic assessment

Authentic assessment methods provide a realistic and meaningful evaluation of student learning by focusing on real-world tasks and applications. 20 Unlike traditional assessments that often rely on multiple-choice questions and rote memorization, authentic assessments require students to demonstrate their knowledge and skills through practical tasks.

Examples of authentic assessments in STEM education include: 20

• Performance tasks
• Presentations
• Capstone projects

Data-driven decision-making

Data-driven decision-making uses assessment data to inform instructional strategies and improve student outcomes. By systematically collecting and analyzing data from various assessments, educators can identify trends, pinpoint areas of improvement, and tailor their teaching to better meet student needs. 21

Step up as an innovative STEM educator

Innovative approaches to STEM teaching are essential for preparing the next generation to navigate and succeed in an increasingly complex and technologically driven world. Join the University of Iowa online to help fill the urgent need for effective STEM teachers. The Online Master of Science in STEM Education is designed for working teachers. Complete your degree in as few as two years with a part-time course load. You will complete one 8-week class at a time, maximizing your ability to focus and collaborate with your peers and faculty members as you balance your outside commitments.

Through the curriculum for the Online MS in STEM Education, you will develop the research and pedagogical competencies to teach the problem-solving and innovation skills your students need to succeed. Earn your master’s from a Big Ten research institution and benefit from our innovative online program and affordable tuition rate for in-state and out-of-state students.

Schedule a call with an admissions outreach advisor today to learn more.

• Retrieved on August 1, 2024, from ncses.nsf.gov/pubs/nsb20245/u-s-stem-workforce-size-growth-and-employment
• Retrieved on August 1, 2024, from nsta.org/nstas-official-positions/stem-education-teaching-and-learning
• Retrieved on August 1, 2024, from engineeringforchange.org/news/heres-engage-stem-students-global-development/
• Retrieved on August 1, 2024, from nextwavestem.com/stem-resources-news/stem-resources-and-news/learning-how-stem-education-is-shaping-your-students-future
• Retrieved on August 1, 2024, from tophat.com/glossary/c/constructivist-pedagogy/
• Retrieved on August 1, 2024, from understood.org/en/articles/differentiated-instruction-what-you-need-to-know
• Retrieved on August 1, 2024, from reflectiveteachingjournal.com/what-is-reflective-teaching/
• Retrieved on August 1, 2024, from pblworks.org/what-is-pbl
• Retrieved on August 1, 2024, from creativeeducator.tech4learning.com/2018/articles/taking-a-project-based-approach-to-STEM-learning
• Retrieved on August 1, 2024, from educationise.com/post/6-steps-to-implement-project-based-learning-in-the-classroom/
• Retrieved on August 1, 2024, from study.com/teach/flipped-classroom.html
• Retrieved on August 1, 2024, from careerfoundry.com/en/blog/ux-design/what-is-design-thinking-everything-you-need-to-know-to-get-started/
• Retrieved on August 1, 2024, from americanmodular.com/your-guide-to-effective-stem-classroom-design/
• Retrieved on August 1, 2024, from education.lego.com/en-us/blog/stem-coding-programming/
• Retrieved on August 1, 2024, from matterandformedu.net/2019/05/22/stem-technology-5-tools-that-will-change-the-classroom/
• Retrieved on August 1, 2024, from blog.kidsparkeducation.org/blog/ai-stem-education-leveraging-technology-to-teach-stem
• Retrieved on August 1, 2024, from robotlab.com/blog/innovative-stem-education-crafting-iot-solutions
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Problem Solving Skills Inspired by STEM Education

STEM (Science, Technology, Engineering, and Mathematics) education serves as a catalyst for developing competent problem solvers who are capable of tackling challenges in a variety of fields. The core principles of STEM education provide the foundation for developing robust problem-solving skills that transcend traditional boundaries.

In an era characterised by technological revolutions, STEM education has become indispensable. It instils in students critical thinking skills, motivating them to inquire, evaluate, and resolve real-world issues. Furthermore, STEM fosters attributes like creativity, teamwork, and adaptability, all of which are vital in a globally interconnected and competitive job market.

Educational Empowerment

 STEM projects place a premium on education as the foundation of their efforts. They work relentlessly to improve STEM education in schools, colleges, and institutions. This involves creating an up-to-date curriculum, improving teaching methods, and offering professional development opportunities for instructors. By providing instructors with the most up-to-date information and tools, they may successfully transfer their enthusiasm for STEM subjects to their pupils.

Inculcating Analytical Thinking 

Teaching Analytical Thinking in STEM education imparts a culture of analytical thinking. Students are encouraged to approach problems systematically and break them down into manageable components. This analytical mindset forms the basis of effective problem-solving, allowing individuals to systematically analyse problems and develop strategic solutions.

Application of Theoretical Knowledge 

Promoting Critical Research The focus of STEM education is the promotion of critical research. Learners are encouraged to ask questions, challenge assumptions and explore innovative approaches. This innate curiosity fosters a problem-solving mindset that seeks comprehensive and efficient solutions to complex challenges rather than just solutions. Application of Theoretical Knowledge STEM Education bridges the gap between theory and application. Practical experience is gained through the practical application of concepts through experiments and projects, allowing students to translate theoretical knowledge into real-world problem-solving scenarios.

Embracing Iterative Problem-Solving 

Comprehensive Iterative Problem-Solving STEM-inspired problem solvers understand the iterative nature of problem-solving. They embrace trial and error and see failure not as a setback but as a stepping stone to further development. This iterative approach fosters resilience and adaptability in dealing with complex problems.

Hands-On Learning

 STEM programmes aggressively promote hands-on learning. This includes giving students and researchers access to cutting-edge facilities, equipment, and Technology. Individuals gain practical insights into scientific topics and engineering principles via hands-on experimentation. They learn not only from textbooks but also through actively participating in their study materials and activities.

Interdisciplinary Approach STEM education

Promotes an interdisciplinary approach to problem-solving.Students integrate knowledge from a variety of STEM fields to develop a comprehensive understanding of the problem. This interdisciplinary perspective widens the range of solutions available to address challenges.

Mentoring and advising

STEM projects frequently match students and prospective researchers with experienced mentors. These mentors provide advice, share their knowledge, and provide essential insights. Mentorship programmes develop creativity and provide a supportive environment for aspiring innovators. This not only recognises accomplishments but also gives a venue for innovators to get feedback, enhance their concepts, and connect with possible partners and investors.

Technological Integration 

Integrating Technology Integrating Technology into his STEM education provides an individual with the skills to utilise technological advances in problem-solving. Knowledge of coding, data analysis, and the use of technical tools improves problem-solving skills and enables innovative and efficient solutions.

Collaborative Problem-Solving 

Problem-solving through collaboration Collaboration is the cornerstone of his STEM education. Students participate in team-based projects and learn how to collaborate effectively. This fosters an environment where a variety of ideas come together and fosters a collaborative problem-solving approach from a variety of perspectives.

Cultivating Resilience and Innovation 

Promoting Resilience and Innovation STEM education strengthens the resilience of problem solvers. Encountering and overcoming challenges fosters innovative thinking. Individuals learn to adapt, innovate, and create unconventional solutions, expanding their problem-solving repertoire.

Fundamentally , STEM education serves as a powerful catalyst for developing problem-solving skills. By emphasising a multifaceted approach, critical thinking, technology integration, and a collaborative spirit, we develop individuals into skilled problem solvers prepared to tackle the complex challenges of today and tomorrow.

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Student-centered active learning improves performance in solving higher-level cognitive questions in health sciences education.

Simple Summary

1. introduction, 2. materials and methods, 2.1. study design, 2.2. theoretical lectures, 2.3. informative sessions, 2.4. student surveys, 2.5. learning outcomes assessment, 3.1. learning outcomes assessment, 3.2. survey conducted during the information session on active learning, 3.2.1. academic year 2022/2023.

• 27% of the students had experience with problem-based learning.
• 72% had experienced collaborative learning.
• 27% were familiar with flipped learning.

3.2.2. Academic Year 2023/2024

• Almost all students indicated they had no prior experience with any form of active learning.
• 79% had experience with collaborative learning.
• 56% knew what flipped learning was.
• 10% of the students chose option (a): “Expository teaching, where the teacher tells me everything I need to know”.
• 89% of students chose option (b): “Active teaching, where I learn to think about and use the content I am learning under the guidance of the teacher”.
• 1% of students chose option (c): “I don’t care, I can always be a GoogleVet”.

3.3. Anonymous Survey Conducted at the End of the Thematic Block

3.4. anonymous survey conducted at the end of the experience, 3.5. students attending to discussion session, 4. discussion, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Share and Cite

Martín-Alguacil, N.; Avedillo, L. Student-Centered Active Learning Improves Performance in Solving Higher-Level Cognitive Questions in Health Sciences Education. Int. Med. Educ. 2024 , 3 , 346-362. https://doi.org/10.3390/ime3030026

Martín-Alguacil N, Avedillo L. Student-Centered Active Learning Improves Performance in Solving Higher-Level Cognitive Questions in Health Sciences Education. International Medical Education . 2024; 3(3):346-362. https://doi.org/10.3390/ime3030026

Martín-Alguacil, Nieves, and Luis Avedillo. 2024. "Student-Centered Active Learning Improves Performance in Solving Higher-Level Cognitive Questions in Health Sciences Education" International Medical Education 3, no. 3: 346-362. https://doi.org/10.3390/ime3030026

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What Are the Possibilities and Challenges Inherent in STEM for Primary Science Teacher Education?

• First Online: 07 September 2024

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• Angela Fitzgerald   ORCID: orcid.org/0000-0001-8010-2215 6 ,
• Kimberley Pressick-Kilborn   ORCID: orcid.org/0000-0003-1662-7038 7 ,
• Reece Mills   ORCID: orcid.org/0000-0002-2156-7677 8 ,
• Linda Pfeiffer   ORCID: orcid.org/0000-0002-5419-4053 9 &
• James Deehan   ORCID: orcid.org/0000-0001-5825-9734 10  

Part of the book series: SpringerBriefs in Education ((BRIEFSEDUCAT))

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There is a Science, Technology, Engineering and Mathematics (STEM) ‘crisis’ which involves a shortage of students—the next generation, who will be responsible for our futures—studying STEM.

As Science, Technology, Engineering, and Mathematics (STEM) education becomes more and more of a focus globally, I see an opportunity for science education to evolve. Primary science teacher education can leverage from the opportunities created by STEM education to create even better teachers and therefore more students who are well-equipped for the challenges of the future. Linda

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Angela Fitzgerald

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Kimberley Pressick-Kilborn

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Fitzgerald, A., Pressick-Kilborn, K., Mills, R., Pfeiffer, L., Deehan, J. (2024). What Are the Possibilities and Challenges Inherent in STEM for Primary Science Teacher Education?. In: Contemporary Australian Primary Science Teacher Education. SpringerBriefs in Education. Springer, Singapore. https://doi.org/10.1007/978-981-97-5660-5_5

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