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ERIC Number: ED593220
Record Type: Non-Journal
Publication Date: 2018-Jul
Pages: 6
Abstractor: As Provided
ISBN: N/A
ISSN: N/A
EISSN: N/A
Mining Student Misconceptions from Pre- and Post-Test Data
Pérez-Lemonche, Ángel; Drury, Byron Coffin; Pritchard, David
International Educational Data Mining Society, Paper presented at the International Conference on Educational Data Mining (EDM) (11th, Raleigh, NC, Jul 16-20, 2018)
We analyze results from paired pre- and post-instruction administration of the Mechanics Baseline Test to 2238 students in introductory mechanics classes. We investigate pairs of specific wrong answers given with unusual frequency by students on the pretest. We also identify transitions between pre- and post-test answers on the same question which elucidate student learning due to instruction. We define criteria for excess transitions above a random response model. Some common transitions are found to be associated specifically with students within a particular range of skills. Further, transitions from pre- to post-test revealed that incorrect pretest answers that were frequently repeated on the posttest often correspond to known misconceptions from physics or math. Thus, our data mining techniques can elucidate common student misunderstandings of mechanics concepts and how instruction affects these misunderstandings. This opens the way for finding improved interventions for specific misunderstandings revealed by analyzing results from pre- and post conceptual tests. [For the full proceedings, see ED593090.]
International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org
Publication Type: Reports - Evaluative
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A