Sentiment Analysis in Learning Management Systems Understanding Student Feedback at Scale
This work addresses the communication gap between instructors and students in online education, particularly during the Covid-19 pandemic, by providing an alternative method to analyze student feedback, though it is incremental in nature.
This paper tackled the problem of interpreting student feedback in online learning environments by integrating sentiment analysis into learning management systems, using a deep neural network model that achieved improved efficacy compared to a logistic regression baseline.
During the wake of the Covid-19 pandemic, the educational paradigm has experienced a major change from in person learning traditional to online platforms. The change of learning convention has impacted the teacher-student especially in non-verbal communication. The absent of non-verbal communication has led to a reliance on verbal feedback which diminished the efficacy of the educational experience. This paper explores the integration of sentiment analysis into learning management systems (LMS) to bridge the student-teacher's gap by offering an alternative approach to interpreting student feedback beyond its verbal context. The research involves data preparation, feature selection, and the development of a deep neural network model encompassing word embedding, LSTM, and attention mechanisms. This model is compared against a logistic regression baseline to evaluate its efficacy in understanding student feedback. The study aims to bridge the communication gap between instructors and students in online learning environments, offering insights into the emotional context of student feedback and ultimately improving the quality of online education.