Multidimensional student skills with collaborative filtering
| Autor: | Saif Rayyan, David E. Pritchard, Yoav Bergner, Daniel T. Seaton |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
business.industry
Computer science Model selection Physics education Machine learning computer.software_genre Data science Test (assessment) Formative assessment Item response theory ComputingMilieux_COMPUTERSANDEDUCATION Collaborative filtering Artificial intelligence business Baseline (configuration management) computer Curse of dimensionality |
| Zdroj: | AIP Conference Proceedings. |
| ISSN: | 0094-243X |
| Popis: | Despite the fact that a physics course typically culminates in one final grade for the student, many instructors and researchers believe that there are multiple skills that students acquire to achieve mastery. Assessment validation and data analysis in general may thus benefit from extension to multidimensional ability. This paper introduces an approach for model determination and dimensionality analysis using collaborative filtering (CF), which is related to factor analysis and item response theory (IRT). Model selection is guided by machine learning perspectives, seeking to maximize the accuracy in predicting which students will answer which items correctly. We apply the CF to response data for the Mechanics Baseline Test and combine the results with prior analysis using unidimensional IRT. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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