Literaturnachweis - Detailanzeige
| Autor/inn/en | Pérez-Lemonche, Ángel; Drury, Byron Coffin; Pritchard, David |
|---|---|
| Titel | Mining Student Misconceptions from Pre- and Post-Test Data |
| Quelle | (2018), (6 Seiten)
PDF als Volltext kostenfreie Datei |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Monographie |
| Schlagwörter | Data Collection; Knowledge Level; Misconceptions; Pretests Posttests; Science Tests; Mechanics (Physics); Introductory Courses; Accuracy; Scientific Concepts; Test Results; College Students; Correlation; Test Items; Robustness (Statistics); Item Response Theory |
| Abstract | 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.] (As Provided). |
| Anmerkungen | International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2020/1/01 |
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