Building an Interpretable Model of Predicting Student Performance Using Comment Data Mining

Abstract

Most current prediction models are difficult for teachers to interpret. This induces significant problems of grasping characteristics for each grade group of students, which are helpful for giving intervention and providing feedback to them. In this paper, we propose a new method to build a practical prediction model based on comment data mining. The current study classifies students' comments into six attributes (attitudes, finding, cooperation, review the lesson, understanding, and next activity plan), then extracts generic rules 'IF-THEN' about students' activities, attitudes and situations in the learning environment. Decision Tree (DT) and Random Forest (RF) models are applied to discriminate unique features related to each grade group. Evaluation results reported a set of rules for students' performance among with their situations reflected through all the course of a semester.