Efficiency of LSA and K-means in predicting students' academic performance based on their comments data

Predicting students' academic performance has long been an important research topic in many academic disciplines. The prediction will help the tutors identify the weak students and help them score better marks; these steps were taken to improve the performance of the students. The present study uses free style comments written by students after each lesson. These comments reflect their learning attitudes to the lesson, understanding of subjects, difficulties to learn, and learning activities in the classroom. (Goda and Mine, 2011) proposed PCN method to estimate students' learning situations from their comments freely written by themselves. This paper uses C (Current) method from the PCN method. The C method only uses comments with C item that focuses on students' understanding and achievements during the class period. The aims of this study are, by applying the method to the students' comments, to clarify relationships between student's behaviour and their success, and to develop a model of students' performance predictors. To this end, we use Latent Semantic Analyses (LSA) and K-means clustering techniques. The results of this study reported a model of students' academic performance predictors by analysing their comment data as variables of predictors.