# Is the Lecture Engaging for Learning? Lecture Voice Sentiment Analysis for Knowledge Graph-Supported Intelligent Lecturing Assistant (ILA) System

arXiv: https://arxiv.org/abs/2408.10492 · HTML: https://mlsanity.com/paper/2408.10492/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 37 references that are in the map.
- First posted on arXiv 20 Aug 2024.

## Builds on (most cited first, 4 of 4)

- [Empowering Personalized Learning through a Conversation-based Tutoring System with Student Modeling](https://mlsanity.com/paper/2403.14071/) (2024)
- [Empowering Private Tutoring by Chaining Large Language Models](https://mlsanity.com/paper/2309.08112/) (2023)
- [Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments](https://mlsanity.com/paper/1812.09628/) (2018)
- [Knowledge Graph Enhanced Intelligent Tutoring System Based on Exercise Representativeness and Informativeness](https://mlsanity.com/paper/2307.15076/) (2023)

## Built on by (most cited first, 2 of 2)

- [Enhancing Student Learning with LLM-Generated Retrieval Practice Questions: An Empirical Study in Data Science Courses](https://mlsanity.com/paper/2507.05629/) (2025)
- [Rate-Distortion Guided Knowledge Graph Construction from Lecture Notes Using Gromov-Wasserstein Optimal Transport](https://mlsanity.com/paper/2511.14595/) (2025)

## Abstract (arXiv)

This paper introduces an intelligent lecturing assistant (ILA) system that utilizes a knowledge graph to represent course content and optimal pedagogical strategies. The system is designed to support instructors in enhancing student learning through real-time analysis of voice, content, and teaching methods. As an initial investigation, we present a case study on lecture voice sentiment analysis, in which we developed a training set comprising over 3,000 one-minute lecture voice clips. Each clip was manually labeled as either engaging or non-engaging. Utilizing this dataset, we constructed and evaluated several classification models based on a variety of features extracted from the voice clips. The results demonstrate promising performance, achieving an F1-score of 90% for boring lectures on an independent set of over 800 test voice clips. This case study lays the groundwork for the development of a more sophisticated model that will integrate content analysis and pedagogical practices. Our ultimate goal is to aid instructors in teaching more engagingly and effectively by leveraging modern artificial intelligence techniques.