Sentiment Analysis in Learning Management Systems Understanding Student Feedback at Scale

2025-06-05

AI Summary

Post-pandemic online education expansion has been hindered by the absence of nonverbal cues, leading to insufficient interpretation of instructors’ and students’ affective intent in textual feedback. To address this, we propose a fine-grained sentiment analysis method embedded within learning management systems (LMS), the first to jointly integrate GloVe word embeddings, bidirectional long short-term memory (BiLSTM), and self-attention mechanisms in an LMS context. Our end-to-end model simultaneously estimates sentiment intensity and pedagogical relevance of student feedback, overcoming limitations of conventional lexical counting and shallow classification approaches. Evaluated on real-world LMS data, the model achieves 89.7% accuracy—outperforming logistic regression baselines by 12.3%. It significantly improves instructors’ timeliness and precision in detecting critical pedagogical signals—including negative affect, confusion, and engagement—thereby offering a deployable technical pathway to enhance online teaching quality.

Technology Category

• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Affective Computing

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Sentiment analysis and opinion mining

Abstract

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.

Problem

• Analyzing student feedback sentiment in online learning systems
• Bridging communication gaps between teachers and students online
• Enhancing online education quality through emotional feedback insights

Innovation

• Integrates sentiment analysis into LMS platforms
• Uses deep neural network with LSTM and attention
• Compares model against logistic regression baseline