assessing creative problem solving with automated text grading

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Assessing creative problem solving with automated text grading

• Computer-aided assessment; Automated grading; Creative problem-solving; Science learning assessment; Machine learning application
• [[classification]]43

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Assessing creative problem-solving with automated text grading

CE 2008 | Natural Language | Open-ended Questions | Science Education |

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Assessing creative problem-solving with automated text grading

• Graduate Institute of Science Education

Research output : Contribution to journal › Article › peer-review

The work aims to improve the assessment of creative problem-solving in science education by employing language technologies and computational-statistical machine learning methods to grade students' natural language responses automatically. To evaluate constructs like creative problem-solving with validity, open-ended questions that elicit students' constructed responses are beneficial. But the high cost required in manually grading constructed responses could become an obstacle in applying open-ended questions. In this study, automated grading schemes have been developed and evaluated in the context of secondary Earth science education. Empirical evaluations revealed that the automated grading schemes may reliably identify domain concepts embedded in students' natural language responses with satisfactory inter-coder agreement against human coding in two sub-tasks of the test (Cohen's Kappa = .65-.72). And when a single holistic score was computed for each student, machine-generated scores achieved high inter-rater reliability against human grading (Pearson's r = .92). The reliable performance in automatic concept identification and numeric grading demonstrates the potential of using automated grading to support the use of open-ended questions in science assessments and enable new technologies for science learning.

• Automated grading
• Computer-aided assessment
• Creative problem-solving
• Machine learning application
• Science learning assessment

ASJC Scopus subject areas

• General Computer Science

Access to Document

• 10.1016/j.compedu.2008.01.006

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• Link to publication in Scopus
• Link to the citations in Scopus

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• education INIS 100%
• assessments INIS 100%
• humans INIS 100%
• natural language INIS 100%
• Problem Social Sciences 100%
• Grading Social Sciences 100%
• Open-Ended Question Psychology 100%
• Assessment Psychology 66%

T1 - Assessing creative problem-solving with automated text grading

AU - Wang, Hao Chuan

AU - Chang, Chun Yen

AU - Li, Tsai Yen

PY - 2008/12

Y1 - 2008/12

N2 - The work aims to improve the assessment of creative problem-solving in science education by employing language technologies and computational-statistical machine learning methods to grade students' natural language responses automatically. To evaluate constructs like creative problem-solving with validity, open-ended questions that elicit students' constructed responses are beneficial. But the high cost required in manually grading constructed responses could become an obstacle in applying open-ended questions. In this study, automated grading schemes have been developed and evaluated in the context of secondary Earth science education. Empirical evaluations revealed that the automated grading schemes may reliably identify domain concepts embedded in students' natural language responses with satisfactory inter-coder agreement against human coding in two sub-tasks of the test (Cohen's Kappa = .65-.72). And when a single holistic score was computed for each student, machine-generated scores achieved high inter-rater reliability against human grading (Pearson's r = .92). The reliable performance in automatic concept identification and numeric grading demonstrates the potential of using automated grading to support the use of open-ended questions in science assessments and enable new technologies for science learning.

AB - The work aims to improve the assessment of creative problem-solving in science education by employing language technologies and computational-statistical machine learning methods to grade students' natural language responses automatically. To evaluate constructs like creative problem-solving with validity, open-ended questions that elicit students' constructed responses are beneficial. But the high cost required in manually grading constructed responses could become an obstacle in applying open-ended questions. In this study, automated grading schemes have been developed and evaluated in the context of secondary Earth science education. Empirical evaluations revealed that the automated grading schemes may reliably identify domain concepts embedded in students' natural language responses with satisfactory inter-coder agreement against human coding in two sub-tasks of the test (Cohen's Kappa = .65-.72). And when a single holistic score was computed for each student, machine-generated scores achieved high inter-rater reliability against human grading (Pearson's r = .92). The reliable performance in automatic concept identification and numeric grading demonstrates the potential of using automated grading to support the use of open-ended questions in science assessments and enable new technologies for science learning.

KW - Automated grading

KW - Computer-aided assessment

KW - Creative problem-solving

KW - Machine learning application

KW - Science learning assessment

UR - http://www.scopus.com/inward/record.url?scp=49449115241&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=49449115241&partnerID=8YFLogxK

U2 - 10.1016/j.compedu.2008.01.006

DO - 10.1016/j.compedu.2008.01.006

M3 - Article

AN - SCOPUS:49449115241

SN - 0360-1315

JO - Computers and Education

JF - Computers and Education

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Computers & Education > 2008 > 51 > 4 > 1450-1466

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Hao-Chuan Wang

• Science Education Center, National Taiwan Normal University, Taipei, Taiwan
• School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, United States

Chun-Yen Chang

• Department of Earth Sciences, National Taiwan Normal University, Taipei, Taiwan

Tsai-Yen Li

• Department of Computer Science, National Chengchi University, Taipei, Taiwan

Computer-aided assessment Automated grading Creative problem-solving Science learning assessment Machine learning application

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