Year: 2025
Type: article
Abstract: Student engagement in college education is a flexible, difficult notion involving participation in interactions, discussion, and performance outcomes in the classroom. It is crucial to anticipate the participation so that the teacher can comprehend whether the student engages with different tasks in the classroom by using fuzzy controllers. In the presence of non-linearities and model hesitations, linear Proportional-Integral-Derivative (PID) controllers may not execute acceptably; hence, fuzzy logic is employed. There isn’t a single, reliable way to tell whether a student has been fully engaged in their college education. The current research aims to choose the most effective fuzzy control algorithm for improving students’ engagement in the classroom. Hence, an Adaptive Mamdani Fuzzy Inference System (AMFIS) based PID controller using the Modified Salp Swarm (MSS) algorithm is proposed. Initially, the AMFIS is applied for expert system applications where the rules are generated from expert teacher knowledge based on student response indicators. The operational input is taken from the membership functions of indicator variables. In this study, a fuzzy PID-type controller is designed and presented to enhance student engagement by providing increased assessments and feedback through an expert decision process. Then, the MSS algorithm is utilized to optimize the scaling variables of membership functions, like improving the assessment counts and reducing the difficulty of curriculum to improve student engagement. The proposed algorithm’s effectiveness is validated using metrics like Cronbach’s alpha coefficient to find the control process’s reliability, accuracy, engagement ratio, and error rate.
Author: Yan Chen
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W4411567137