Summary of Llm-sem: a Sentiment-based Student Engagement Metric Using Llms For E-learning Platforms, by Ali Hamdi et al.
LLM-SEM: A Sentiment-Based Student Engagement Metric Using LLMS for E-Learning Platforms
by Ali Hamdi, Ahmed Abdelmoneim Mazrou, Mohamed Shaltout
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces LLM-SEM (Language Model-Based Student Engagement Metric), a novel approach for analyzing student engagement in e-learning platforms. By leveraging video metadata and sentiment analysis of student comments, the method mitigates text fuzziness and normalizes key features such as views and likes. The technique combines comprehensive metadata with sentiment polarity scores to gauge engagement at both course and lesson levels. Experiments demonstrate the effectiveness of LLM-SEM in providing a scalable and accurate measure of student engagement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding new ways to measure how engaged students are when they’re learning online. Right now, we don’t have great methods for doing this because it’s hard to understand what students mean when they write comments, and we often only have limited information to work with. The authors of this paper came up with a new approach called LLM-SEM that uses special computer models to analyze video data and student comments to figure out how engaged students are. They tested different models and showed that their method is better than what we’re using now. |
Keywords
» Artificial intelligence » Language model
