Predicting Student Success using Fine Grain Clicker Data

Publisher:
The Association for Computing Machinery
Publication Type:
Conference Proceeding
Citation:
ICER14: Proceedings of the Tenth Annual International Conference on International Computing Education Research, 2014, pp. 51 - 58
Issue Date:
2014-01
Full metadata record
Abstract: Recent research suggests that the rst weeks of a CS1 course have a strong in influence on end-of-course student performance. The present work aims to rene 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 performance 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.
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