Assessing Student Self-Explanations in an Intelligent Tutoring System

Research indicates that guided feedback facilitates learning, whether in the classroom or with Intelligent Tutoring Systems (ITS). Improving the accuracy of the evaluation of user input is therefore necessary for providing optimal feedback. This study investigated an automated assessment of students’ input that involved a lexico-syntactic (entailment) approach to textual analysis along with a variety of other textual assessment measures. The corpus consisted of 357 student responses taken from a recent experiment with iSTART, an ITS that provides students with self-explanation and reading strategy training. The results of our study indicated that the entailment approach provided the highest single measure of accuracy for assessing input when compared to the other measures in the study. A set of indices working in conjunction with the entailment approach provided the best overall assessments.