Predicting student success using fine grain clicker data
- Publication Type:
- Conference Proceeding
- ICER 2014 - Proceedings of the 10th Annual International Conference on International Computing Education Research, 2014, pp. 51 - 58
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Recent research suggests that the first weeks of a CS1 course have a strong inuence on end-of-course student performance. The present work aims to refine the understanding of this phenomenon by using in-class clicker questions as a source of student performance. Clicker questions generate per-lecture and per-question data with which to assess student under- standing. This work demonstrates that clicker question per- formance early in the term predicts student outcomes at the end of the term. The predictive nature of these questions applies to code-writing questions, multiple choice questions, and the final exam as a whole. The most predictive clicker questions are identified and the relationships between these questions and final exam performance are examined. Copyright © 2014 ACM.
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