Predicting Students' Retention of Facts from Feedback during Study


Testing students as they study a set of facts is known to enhance their learning (Roediger & Karpicke, 2006). Testing also provides tutoring software with potentially valuable information regarding the extent to which a student has mastered study material. This information, consisting of recall accuracies and response latencies, can in principle be used by tutoring software to provide students with individualized instruction by allocating a student's time to the facts whose further study it predicts would provide greatest benefit. In this paper, we propose and evaluate several algorithms that tackle the benefit-prediction aspect of this goal. Each algorithm is tasked with calculating the likelihood a student will recall facts in the future given recall accuracy and response latencies observed in the past. The disparate algorithms we tried, which range from logistic regression to a Bayesian extension of the ACT-R declarative memory module, proved to all be roughly equivalent in their predictive power. Our modeling work demonstrates that, although response latency is predictive of future test performance, it yields no predictive power beyond that which is held in response accuracy.

Back to Table of Contents