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Interactive and Visualized Online Experimentation System for Engineering Education and Research
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Integration of artificial intelligence performance prediction and learning analytics to improve student learning in
Fan Ouyang1, Mian Wu1, Luyi Zheng1
1College of Education, Zhejiang University, Hangzhou, 310058 Zhejiang China.
International Journal of Educational Technology in Higher Education
|January 23, 2023
Summary
This study integrates artificial intelligence (AI) performance prediction with learning analytics (LA) to enhance collaborative learning. The combined approach improved student engagement, performance, and satisfaction in an online engineering course.
Area of Science:
- Artificial Intelligence in Education (AIEd)
- Learning Analytics (LA)
Background:
- Existing AI performance prediction models prioritize algorithm accuracy over real-time student feedback.
- There is a need to improve student learning quality through timely, continuous feedback in AIEd.
Purpose of the Study:
- To integrate AI performance prediction models with learning analytics approaches.
- To enhance student learning effects within a collaborative learning context.
Main Methods:
- A quasi-experimental design was employed in an online engineering course.
- Student learning effects were compared between groups with and without the integrated AI and LA approach.
Main Results:
- The integrated approach significantly increased student engagement.
- Collaborative learning performances were notably improved.
- Student satisfaction with the learning experience was strengthened.
Conclusions:
- Integrating AI performance prediction with LA feedback offers a novel approach to improving collaborative learning.
- This research provides a paradigm for developing AI-driven learning analytics systems.
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