Correlation of Grade Prediction Performance with Characteristics of Lesson Subject

Abstract

Learning analytics is valuable sources of understanding students' behavior and giving feedback to them so that we can improve their learning activities. Analyzing comment data written by students after each lesson helps to grasp their learning attitudes and situations. They can be a powerful source of data for all forms of assessment. In the current study, we break down student comments into different topics by employing two topic models: Probabilistic Latent Semantic Analysis (PLSA), and Latent Dirichlet Allocation (LDA), to discover the topics that help to predict final student grades as their performance. The objectives of this paper are twofold: First, determine how the three time-series items: P-, C- and N-comments and the difficulty of a subject affect the prediction results of final student grades. Second, evaluate the reliability of predicting student grades by considering the differences between prediction results of two consecutive lessons. The results obtained can help to understand student behavior during the period of the semester, grasp prediction error occurred in each lesson, and achieve further improvement of the student grade prediction.