Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{175870,
        author = {Aneesh Gupta and Deepak Yadav and Prikshit Juneja and Shampa Chakraverty},
        title = {Estimating Classroom Engagement through Audio-Based Signal Processing and Interaction Analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {4559-4564},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175870},
        abstract = {Classroom engagement is an essential element of good pedagogy, however it continues to pose difficulties in objective measurement and analysis. This study shows a new way to measure student involvement in the classroom using audio input. The goal is to give the teachers useful information they can use to improve the way they teach. The suggested method looks at audio from classrooms to create a "interactivity score," which gives a full picture of how teachers and students talk to each other. This analysis makes thorough summaries that include the number of interactions, the tone of the conversations, the topics that were talked about, who was talking, and how long and how important the conversations were. Beyond conventional classrooms, this research finds value in many fields including university and school teaching, army training camps, faculty development programs (FDPs), Corporate Training Sessions, Online Learning Platforms etc. These realizations can enable teachers to improve their instructional strategies so guaranteeing efficient knowledge transmit and skill development.
This research employs sophisticated signal processing methodologies, encompassing spectral analysis via Mel-frequency cepstral coefficients (MFCCs), clustering algorithms for speaker recognition, and natural language processing (NLP) for sentiment and topic evaluation. The results emphasize the promise of audio-based engagement analysis as a revolutionary instrument in contemporary education and training, facilitating data-driven enhancements in teaching and learning experiences.},
        keywords = {audio analysis, classroom dynamics, classroom interactivity, education enhancement, educational technology, engagement, engagement detection, interactivity analysis, mel-frequency cepstral coefficients, speaker diarization},
        month = {April},
        }