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{182752,
        author = {Swapnil Gaikwad and Abhishek Nagare},
        title = {Using Regression & Clustering Algorithm to Classify Student Attentiveness},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {4436-4438},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182752},
        abstract = {There have been multiple studies where researchers have tried to classify student attentiveness. Numerous of these approaches depended on a qualitative examination and lacked any quantitative examination. This study bridges the gap be- tween qualitative and quantitative approaches to classify student attentiveness. The findings from this study can help teachers at all levels improve their teaching methods and implement individualized learning. This study uses video data as input, employing machine learning methods such as linear regression and hierarchical clustering to automatically classify students as attentive or inattentive. Machine learning algorithms are used to train classifiers that estimate time-varying attention levels of individual students. Human-observed attention levels serve as a benchmark for comparison. This activity improves students’ ability to perform in their respective fields and contributes to the development of effective educational methodologies.},
        keywords = {Linear Regression, Hierarchical Clustering, K- Means, Personalized Learning System},
        month = {July},
        }