A Study on the Impact of Multi-Agent-Supported 4C/ID Learning Approach on Vocational Students' Learning Outcomes

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  In Roundtable Session: Advancing Equity through Instructional Design and Technology

Abstract

The advancement of intelligent technologies is fundamentally reshaping educational processes and instructional methods. This study introduces a Multi-Agent-Supported Four-Component Instructional Design (MA-4C/ID) learning approach by integrating multi-agent systems into the 4C/ID model, and investigates its effects on vocational students' learning performance compared to the Conventional 4C/ID (C-4C/ID) approach. A quasi-experimental study involving 67 vocational students demonstrated that the MA-4C/ID approach significantly improved learning achievement, reduced cognitive load, and enhanced learning attitudes compared to the C-4C/ID approach. These findings underscore the effectiveness of the MA-4C/ID approach in improving vocational education and provide robust support for 4C/ID model innovation in the intelligent technology era.

Authors

• Changchun Bi, East China Normal University

• Haoming Wang, East China Normal University

• Yang Hong, East China Normal University

• Fanfei MENG, University of Pennsylvania

• Chunjia Bao, National University of Singapore

• Xianlong Xu, East China Normal University