Towards Adaptive Provision of Examples During Problem Solving

Abstract

Intelligent Tutoring Systems (ITSs) are effective in supporting learning, as shown in numerous studies. The goal of our project is to develop an adaptive strategy that would be capable of identifying situations during problem solving in which the student would benefit from worked examples. As a first step towards developing such a strategy, we conducted a pilot study in the context of SQL-Tutor, a mature ITS that teaches database querying. The participant could ask for a worked example whenever he/she wanted during problems solving. After each example, the participant specified whether the example was useful, and whether additional examples were needed. Participants’ facial expressions and eye gaze were recorded. The findings show that the participants generally found examples useful, although in some cases they stated additional examples would be beneficial. The analysis of the eye gaze shows that students compared provided examples to their own solutions. Affect analysis shows that negative emotions reduced while engagement increased when participants viewed examples, and immediately after examples.