Date of Original Version
Proceedings of the 1st International Conference on Educational Data Mining, 2008, p. 117-126
Abstract or Table of Contents
Students can use an educational system's help in unexpected ways. For example, they may bypass abstract hints in search of a concrete solution. This behavior has traditionally been labeled as a form of gaming or help abuse. We propose that some examples of this behavior are not abusive and that bottom-out hints can act as worked examples. We create a model for distinguishing good student use of bottom-out hints from bad student use of bottom-out hints by means of logged response times. We show that this model not only predicts learning, but captures behaviors related to self-explanation.