Teacher interventions to enhance the quality of student comments and their effect on prediction performance

Today, the use of learning analytics is becoming more crucial in the learning environment for the purpose of understanding and optimizing students' learning situations. The purpose of this paper is to examine the impacts of Teacher Interventions (TIs) on students' attitudes and achievements involved with the lesson by analyzing their freestyle comment data after every lesson. The current study proposes a new method for building an accessible prediction model, which represents students' activities, situations and viewpoints; the method classifies words in the student comments into six attribute types and indicates the most important types that affect the prediction results. Further, the prediction results are compared with the topic-based statistical method that uses Latent Dirichlet Allocation and Support Vector Machine models. The results proved that there were positive correlations between TIs and the quality of writing comments that affect on improving the prediction results.