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

TL;DR

This paper presents a case study on voice sentiment analysis in lectures, aiming to determine engagement levels using machine learning models trained on labeled voice clips, to support more engaging teaching methods.

Contribution

It introduces a dataset and classification models for lecture voice sentiment analysis, demonstrating promising results and laying groundwork for more comprehensive intelligent lecturing systems.

Findings

Achieved 90% F1-score in classifying boring lectures

Developed a dataset of over 3,000 labeled voice clips

Established a foundation for integrating content and pedagogical analysis

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.