A Concept: Classifying Student's Feedback Electronically for Improving Academics

Abstract

Student's feedback on academics can provide valuable information for an institution for finding their current teaching practices quality and, also can provide suggestions for improving the overall teaching process. There are various kinds of student feedback systems used in different institutions and are mostly manual based process. Therefore, the study proposes a concept of computerized student feedback system (SFS) for academics. Student generally provides their feedback using open ended sentences. SFS will classify all feedback into several categories such as positive, negative, neutral. The proposed SFS uses a combination of machine learning's rule-based and lexicon based custom corpus datastore. This system will be implemented and evaluated in various educational institutions to test the effectiveness of SFS in the academic's context.