ACME: automated classification model for E-learning feedback

Abstract

ABSTRACT E-learning is the process of sharing knowledge out of the traditional classrooms through different online tools using internet. The availability and use of these tools are not easy for every student. Many institutions gather e-learning feedback to know the problems of students to improve their systems. In e-learning systems, typically a high volume of electronic feedback is received every day. In order to respond to feedback quickly and to address the issues raised by the stakeholders timely, it is desirable to provide an intelligent and automatic classification of the feedback. This study proposes a feedback classification model, named “ACME: Automated Classification Model for E-learning Feedback” to fulfill this need. The proposed model is capable of both sentiment analysis and category classification of the feedback. In this regard, feedback from 20,000 records has been collected from many online sources, such as social media platforms, surveys, and questionnaires. To build a baseline corpus, these feedbacks were analyzed and characterized in two dimensions, that is category and sentiment. The model achieved an accuracy of 98% and 99% by using a Random Forest and Decision Tree classifier, respectively.