Bayesian Asymmetric Quantized Neural Networks Students Behaviour Prediction with feedback generation for Enhancing Classroom Engagement and Teaching Effectiveness with Snow Geese Algorithm

DOI

10.2991/978-94-6463-662-8_27How to use a DOI?

Keywords

Savitzky-Golay Denoising method (SGDM); Adaptive Quantization for a Discrete Wavelet Transformation’s Coefficients (AQDWTC); using Giant Trevally Optimizer (GTO); Bayesian asymmetric quantized neural networks (BAQNN) and Snow Geese Algorithm (SGA)

Abstract

Data-driven models are used in Students’ Behavior Prediction with Feedback Generation to predict learning demands and student behaviors. It offers individualized feedback to boost classroom involvement and teaching effectiveness by assessing engagement patterns and performance. The goal is to improve student outcomes and customizes instructional tactics to meet the needs of each individual student. To overcome these problems, Bayesian asymmetric quantized neural networks with Snow Geese Algorithm (BAQNN-SGA) is proposed. In this, input data is taken from a dataset such as OULAD datasets. It pre-processed data using Savitzky-Golay Denoising method (SGDM), Following that, Students Behaviour Prediction for Enhancing Classroom Engagement and Teaching Effectiveness is then the features extracted using Adaptive Quantization for a Discrete Wavelet Transformation’s Coefficients (AQDWTC), Following that Feature selection using Giant Trevally Optimizer (GTO) and classification Bayesian asymmetric quantized neural networks (BAQNN) and the optimization using Snow Geese Algorithm (SGA) for detecting the type of Students Behaviour Prediction and to find the Low achiever, Medium, High achiever Engagement. The introduced system is executed in python. The efficiency of the proposed BAQNN-SGA is analysed using a datasets and attains 99.9% accuracy, 99.8% recall and attains better results compared with the existing methods. This indicates the approach’s superior efficiency and potential for further development in the field.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - S. Zahoor Ul Huq
AU  - S. Shabana Begum
AU  - K. Bala Chowdappa
AU  - K. Ishthaq Ahamed
AU  - K. V. Rameswara Reddy
PY  - 2025
DA  - 2025/03/17
TI  - Bayesian Asymmetric Quantized Neural Networks Students Behaviour Prediction with feedback generation for Enhancing Classroom Engagement and Teaching Effectiveness with Snow Geese Algorithm
BT  - Proceedings of the International Conference on Advanced Materials, Manufacturing and Sustainable Development (ICAMMSD 2024)
PB  - Atlantis Press
SP  - 329
EP  - 342
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-662-8_27
DO  - 10.2991/978-94-6463-662-8_27
ID  - Huq2025
ER  -