Predicting User Satisfaction in Online Education Platforms: A Large Language Model Based Multi-Modal Review Mining Framework

TL;DR

This paper introduces a multi-modal framework leveraging large language models to accurately predict learner satisfaction by integrating textual, sentiment, and behavioral data from online education reviews.

Contribution

It presents a novel LLM-based multi-modal approach that combines thematic, sentiment, and behavioral features for satisfaction prediction in online education.

Findings

The framework outperforms traditional models in satisfaction prediction accuracy.

Joint modeling of topics, sentiment, and behavior improves results.

Large-scale contextual language representations are crucial for learning analytics.

Abstract

Online education platforms have experienced explosive growth over the past decade, generating massive volumes of user-generated content in the form of reviews, ratings, and behavioral logs. These heterogeneous signals provide unprecedented opportunities for understanding learner satisfaction, which is a critical determinant of course retention, engagement, and long-term learning outcomes. However, accurately predicting satisfaction remains challenging due to the short length, noise, contextual dependency, and multi-dimensional nature of online reviews. In this paper, we propose a unified \textbf{Large Language Model (LLM)-based multi-modal framework} for predicting both platform-level and course-level learner satisfaction. The proposed framework integrates three complementary information sources: (1) short-text topic distributions that capture latent thematic structures, (2) contextualized sentiment representations learned from pretrained Transformer-based language models, and (3) behavioral interaction features derived from learner activity logs. These heterogeneous representations are fused within a hybrid regression architecture to produce accurate satisfaction predictions. We conduct extensive experiments on large-scale MOOC review datasets collected from multiple public platforms. The experimental results demonstrate that the proposed LLM-based multi-modal framework consistently outperforms traditional text-only models, shallow sentiment baselines, and single-modality regression approaches. Comprehensive ablation studies further validate the necessity of jointly modeling topic semantics, deep sentiment representations, and behavioral analytics. Our findings highlight the critical role of large-scale contextual language representations in advancing learning analytics and provide actionable insights for platform design, course improvement, and personalized recommendation.