Correlation of topic model and student grades using comment data mining

Assessment of learning progress and learning gain play a pivotal role in education fields. New technologies like comment data mining promote the use of new types of contents; student comments highly reflect student learning attitudes and activities compared to more traditional methods and they can be a powerful source of data for all forms of assessment. A teacher just asks students after every lesson to freely describe and write about their learning situations and behaviors. This paper proposes new methods based on a statistical latent class "Topics" for the task of student grade prediction; our methods convert student comments using latent semantic analysis (LSA) and probabilistic latent semantic analysis (PLSA), and generate prediction models using support vector machine (SVM) and artificial neural network (ANN) to predict student final grades. The experimental results show that our methods can accurately predict student grades based on comment data.