Workshop on Non-Cognitive Factors & Personalization for Adaptive Learning (NCFPAL)

Intelligent tutoring systems (ITSs) that personalize instruction to individual learner background and preferences have emerged in K-16 classroom settings all over the world. In mathematics instruction, ITSs may be especially important for tracking mathematical skill development over time. However, recent research has pointed to the importance of text-based measures when solving mathematics word problems, suggesting that in order to accurately model the student it is important to understand how they respond to text characteristics. We investigate the impact of text-based factors (readability and problem topic) on the solving of mathematics story problems using a corpus of N = 3394 students working through an ITS for algebra, Cognitive Tutor Algebra. We leverage recent advances in computerized text-mining to automate fine-grained text analyses of many different word problems. We find that several elements of the text of mathematics word problems matter for performance – including the concreteness of the problem’s topic, the length and conciseness of the story’s text, and the words and phrases used.