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| Main Authors: | , |
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| Format: | Preprint |
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2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.11723 |
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| _version_ | 1866913026221801472 |
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| author | Bekov, Arman Nurgali, Azamat |
| author_facet | Bekov, Arman Nurgali, Azamat |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_11723 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Predicting User Satisfaction in Online Education Platforms: A Large Language Model Based Multi-Modal Review Mining Framework Bekov, Arman Nurgali, Azamat Graphics 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. |
| title | Predicting User Satisfaction in Online Education Platforms: A Large Language Model Based Multi-Modal Review Mining Framework |
| topic | Graphics |
| url | https://arxiv.org/abs/2604.11723 |