EMOTION MINING FROM STUDENT COMMENTS A LEXICON BASED APPROACH FOR PEDAGOGICAL INNOVATION ASSESSMENT

Abstract

Course evaluation provided by student’s play a major role in a wide range of factors that include suggestions on areas of improvement in terms of teaching, available resources, study environment, and student assessment techniques. These evaluations are collected in both quantitative and qualitative forms. The quantitative feedbacks include a Likert-type scale in which responses are scored along a range, to capture the level of agreement and disagreement. Whereas the qualitative feedbacks provide an open portal for the students to convey their feelings, thoughts or opinion about the course, instructor and assessments in a more general way. The qualitative data is in the form of textual comments which can be processed to mine student’s emotional feeling and gain more intellectual insights. In this work we focus on qualitative student feedbacks through text mining and sentiment analysis. We analyze the efficiency of Active Learning methods Light Weight teams and Flipped Classroom. Results show the implementation of these methods is linked with increased positivity in student emotions.