Article: Particle swarm optimisation-based self-efficacy model for student learning and decision-making capabilities Journal: International Journal of Computational Systems Engineering (IJCSYSE) 2025 Vol.9 No.10 pp.1 - 9 Abstract: Improving the model's structural validity and reliability requires taking into account the students' implicit relationship with the decision-making process about their professions. With a self-efficacy model based on social cognitive theory, this article aims to help students interested in education make more informed career selections. This study aims to evaluate the social cognitive theory-based self-efficacy model in order to find its distinctive elements. If you want to help your pupils find a job that fits their hidden talents, you may utilise their implicit feature matrix. When trainees make professional decisions, a supplemental matrix is used to investigate the hidden relationships among them. Compared to its alternatives - which relied on cluster analysis and the user portrait method - this model exhibited superior structural validity and dependability. The successful model validation provided evidence of this. Therefore, it is a reliable measure of students' confidence in their ability to make good career decisions down the road. Inderscience Publishers - linking academia, business and industry through research

Title: Particle swarm optimisation-based self-efficacy model for student learning and decision-making capabilities

Authors: Qing Zhou

Addresses: Chongqing Industry Polytechnic College, ChongQing, 401120, China

Abstract: Improving the model's structural validity and reliability requires taking into account the students' implicit relationship with the decision-making process about their professions. With a self-efficacy model based on social cognitive theory, this article aims to help students interested in education make more informed career selections. This study aims to evaluate the social cognitive theory-based self-efficacy model in order to find its distinctive elements. If you want to help your pupils find a job that fits their hidden talents, you may utilise their implicit feature matrix. When trainees make professional decisions, a supplemental matrix is used to investigate the hidden relationships among them. Compared to its alternatives - which relied on cluster analysis and the user portrait method - this model exhibited superior structural validity and dependability. The successful model validation provided evidence of this. Therefore, it is a reliable measure of students' confidence in their ability to make good career decisions down the road.

Keywords: social cognitive theory; students; self-efficacy; decision making; particle swarm optimisation; PSO.

DOI: 10.1504/IJCSYSE.2025.146794

International Journal of Computational Systems Engineering, 2025 Vol.9 No.10, pp.1 - 9

Received: 29 Dec 2023
Accepted: 05 Feb 2024
Published online: 18 Jun 2025 *

Title: Particle swarm optimisation-based self-efficacy model for student learning and decision-making capabilities

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