# LLM-SEM: A Sentiment-Based Student Engagement Metric Using LLMS for E-Learning Platforms

arXiv: https://arxiv.org/abs/2412.13765 · HTML: https://mlsanity.com/paper/2412.13765/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 18 Dec 2024.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Gemma 2: Improving Open Language Models at a Practical Size](https://mlsanity.com/paper/2408.00118/) (2024)
- [TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification](https://mlsanity.com/paper/2010.12421/) (2020)
- [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020)
- [Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models](https://mlsanity.com/paper/2310.04027/) (2023)
- [GPT Models in Construction Industry: Opportunities, Limitations, and a Use Case Validation](https://mlsanity.com/paper/2305.18997/) (2023)
- [ASEM: Enhancing Empathy in Chatbot through Attention-based Sentiment and Emotion Modeling](https://mlsanity.com/paper/2402.16194/) (2024)

## Built on by (most cited first, 6 of 6)

- [Retrieval Augmented Generation Based LLM Evaluation For Protocol State Machine Inference With Chain-of-Thought Reasoning](https://mlsanity.com/paper/2502.15727/) (2025)
- [An Ensemble Classification Approach in A Multi-Layered Large Language Model Framework for Disease Prediction](https://mlsanity.com/paper/2509.02446/) (2025)
- [Arabic Large Language Models for Medical Text Generation](https://mlsanity.com/paper/2509.10095/) (2025)
- [Scaling Arabic Medical Chatbots Using Synthetic Data: Enhancing Generative AI with Synthetic Patient Records](https://mlsanity.com/paper/2509.10108/) (2025)
- [Improving Answer Extraction in Context-based Question Answering Systems Using LLMs](https://mlsanity.com/paper/2606.06197/) (2026)
- [A Self Consistency Based Reranking for Narrative Question Answering](https://mlsanity.com/paper/2606.15741/) (2026)

## Abstract (arXiv)

Current methods for analyzing student engagement in e-learning platforms, including automated systems, often struggle with challenges such as handling fuzzy sentiment in text comments and relying on limited metadata. Traditional approaches, such as surveys and questionnaires, also face issues like small sample sizes and scalability. In this paper, we introduce LLM-SEM (Language Model-Based Student Engagement Metric), a novel approach that leverages video metadata and sentiment analysis of student comments to measure engagement. By utilizing recent Large Language Models (LLMs), we generate high-quality sentiment predictions to mitigate text fuzziness and normalize key features such as views and likes. Our holistic method combines comprehensive metadata with sentiment polarity scores to gauge engagement at both the course and lesson levels. Extensive experiments were conducted to evaluate various LLM models, demonstrating the effectiveness of LLM-SEM in providing a scalable and accurate measure of student engagement. We fine-tuned TXLM-RoBERTa using human-annotated sentiment datasets to enhance prediction accuracy and utilized LLama 3B, and Gemma 9B from Ollama.