Exploring Academicians' Behavioral Intention Towards Blended Learning System: ANN Approach

• Published in: 2024 5th International Conference on Artificial Intelligence and Data Sciences, AiDAS 2024 - Proceedings
• Main Author: Sarkam N.A.; Mohammad N.H.; Jamil N.I.; Razi N.F.M.; Ahmad S.; Ridzuan A.N.A.A.
• Format: Conference paper
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209684221&doi=10.1109%2fAiDAS63860.2024.10730673&partnerID=40&md5=40e5f374166dd34f930011ce1df619a5

The integration of Information and Communication Technology (ICT) into educational methodologies has become a focal point for scholars globally. This study aims to explore the factors influencing academicians' behavioral intention towards the adoption of blended learning (BL) systems using an Artificial Neural Network (ANN) approach. The methodology involved utilizing secondary data from previous studies, consisting of 200 academicians from higher education institutions in Malaysia. The dataset included demographic information and six constructs: performance expectancy, effort expectancy, perceived playfulness, facilitating conditions, social influence, and behavioral intention. Pearson correlation analysis was conducted to understand relationships between variables, followed by the development of an ANN model using a multilayer perceptron architecture. The results indicated that performance expectancy and perceived playfulness are the most influential factors affecting academicians' intentions to adopt BL systems. The ANN model demonstrated superior predictive accuracy compared to traditional regression models. In conclusion, the application of ANN provides a comprehensive understanding of the factors influencing academicians' behavioral intentions towards BL systems, making it a valuable tool for educational research and policy formulation. © 2024 IEEE.