Abstract
The artificial intelligence methods might be applied to see through the education problems, and make effective prediction and decision. The transformation from data to decision are inseparable from the learning analytics. In order to solve the dynamic multi-objective decision problems, a decision learning algorithm is designed to analyze the learning behavior and learning achievement based on the particle swarm optimization mechanism, which ensures the global optimization of data analysis. Compared with the performance indicators of several approximate algorithms, this algorithm has the advantage of association analysis, which improves the accuracy and robustness; Furthermore, three tested problems are constructed by GeoDa tool, and through data training and analysis, the dynamic multi-objective decision rules are obtained. The whole research can provide theoretical and technical support for other related or similar issues.
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Acknowledgements
Thanks for the technical support provided by the laboratory of School of Software of Tsinghua University (China), as well as the theoretical guidance and practical reference of University of Agder (Norway) and Qufu Normal University (China).
Funding
This study is supported by Social Science Planning Project of Shandong Province (Grant No. 22CJYJ20).
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Xia, X., Wang, T. Multi Objective Evaluation Between Learning Behavior and Learning Achievement. Asia-Pacific Edu Res 33, 1–15 (2024). https://doi.org/10.1007/s40299-022-00703-z
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DOI: https://doi.org/10.1007/s40299-022-00703-z