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Preprint
Is the Lecture Engaging for Learning? Lecture Voice Sentiment Analysis for Knowledge Graph-Supported Intelligent Lecturing Assistant (ILA) System
ArXiv.org
19 Aug 2024
Abstract
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.
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Details
- Title
- Is the Lecture Engaging for Learning? Lecture Voice Sentiment Analysis for Knowledge Graph-Supported Intelligent Lecturing Assistant (ILA) System
- Creators
- Yuan AnSamarth KolanupakaJacob AnMatthew MaUnnat ChhatwalAlex KalinowskiMichelle RogersBrian Smith
- Publication Details
- ArXiv.org
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Information Science
- Other Identifier
- 991021899315604721