Classification of Students' Facial Expressions Utilizing Convolutional Kolmogorov–Arnold Networks (C-KANs) and Classroom Teaching Methods Classification Employing ResNet-152

Hutami Endang (Institut Teknologi dan Bisnis Kalla, Indonesia)
Annahl Riadi (Universitas Ichsan Sidenreng Rappang, Indonesia)
Alif Fauzan (Universitas Hasanuddin, Indonesia)
Andi Jamiati Paramita (Institut Teknologi dan Bisnis Kalla, Indonesia)
Furqan Zakiyabarsi (Institut Teknologi dan Bisnis Kalla, Indonesia)
Hany Alexanders (Institut Teknologi dan Bisnis Kalla, Indonesia)

Journal of Vocational, Informatics and Computer Education
Vol 4, No 2 (2026): June 2026

Purpose - This study proposes a deep learning parallel dual-classification framework utilizing a lightweight Convolutional Kolmogorov–Arnold Network (C-KAN) to recognize six classes of students' facial expressions and a ResNet-152 to classify three types of teaching methods.Methods - The parallel framework routes micro-student expressions and macro-classroom contexts separately. The C-KAN model features a two-pronged architecture combining 96×96 pixel grayscale facial images with 9 geometric features from 68 landmarks, while ResNet-152 processes 256×256 pixel RGB full-classroom frames. Findings - Evaluated on elementary school recordings (9,130 teaching method images and 2,069 facial expression instances), ResNet-152 achieved 95% accuracy (0.94 macro F1-score). Meanwhile, C-KAN achieved 87% accuracy (0.86 macro F1-score) across six facial expressions.Research implications - By leveraging learnable B-spline functions, C-KAN models complex non-linear micro-expressions accurately with only 4.1 million parameters. This structural efficiency slashes inference time to 11 ms, proving its high viability for real-time edge-computing analytics.Originality - His framework introduces a dual-perspective routing approach. Integrating spline-based C-KAN provides high discriminative power with low computational overhead, supporting evidence-based, automatic evaluation of classroom learning.