Educational Practices and Algorithmic Framework for Promoting Sustainable Development in Education by Identifying Real-World Learning Paths

Tian-Yi Liu, Yuan-Hao Jiang, Yuang Wei, Xun Wang (), Shucheng Huang and Ling Dai ()
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Tian-Yi Liu: School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212100, China
Yuan-Hao Jiang: Lab of Artificial Intelligence for Education, East China Normal University, Shanghai 200062, China
Yuang Wei: Lab of Artificial Intelligence for Education, East China Normal University, Shanghai 200062, China
Xun Wang: School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212100, China
Shucheng Huang: School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212100, China
Ling Dai: Department of Education, East China Normal University, Shanghai 200062, China

Sustainability, 2024, vol. 16, issue 16, 1-26

Abstract: Utilizing big data and artificial intelligence technologies, we developed the Collaborative Structure Search Framework (CSSF) algorithm to analyze students’ learning paths from real-world data to determine the optimal sequence of learning knowledge components. This study enhances sustainability and balance in education by identifying students’ learning paths. This allows teachers and intelligent systems to understand students’ strengths and weaknesses, thereby providing personalized teaching plans and improving educational outcomes. Identifying causal relationships within knowledge structures helps teachers pinpoint and address learning issues, forming the basis for adaptive learning systems. Using real educational datasets, the research introduces a multi-sub-population collaborative search mechanism to enhance search efficiency by maintaining individual-level superiority, population-level diversity, and solution-set simplicity across sub-populations. A bidirectional feedback mechanism is implemented to discern high-quality and low-quality edges within the knowledge graph. Oversampling high-quality edges and undersampling low-quality edges address optimization challenges in Learning Path Recognition (LPR) due to edge sparsity. The proposed Collaborative Structural Search Framework (CSSF) effectively uncovers relationships within knowledge structures. Experimental validations on real-world datasets show CSSF’s effectiveness, with a 14.41% improvement in F1-score over benchmark algorithms on a dataset of 116 knowledge structures. The algorithm helps teachers identify the root causes of students’ errors, enabling more effective educational strategies, thus enhancing educational quality and learning outcomes. Intelligent education systems can better adapt to individual student needs, providing personalized learning resources, facilitating a positive learning cycle, and promoting sustainable education development.

Keywords: sustainable development in education; AI for education; educational application; learning path recognition; evolutionary computation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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