Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course

International Journal of Educational Technology in Higher Education

Research Article

Fan Ouyang¹  Mian Wu¹  Liyin Zhang¹  Luyi Zheng¹  Pengcheng Jiao² 

[1] College of Education, Zhejiang University, 310058, Hangzhou, Zhejiang, China;Institute of Port, Coastal and Offshore Engineering, Ocean College, Zhejiang University, 316021, Zhoushan, Zhejiang, China;

关键词: Artificial intelligence in education (AIEd);    Academic performance prediction;    AI prediction models;    Collaborative learning;    Online higher education;   

DOI  :  10.1186/s41239-022-00372-4

 received in 2022-09-07, accepted in 2022-11-07,  发布年份 2022

来源: Springer

【 摘 要 】

As a cutting-edge field of artificial intelligence in education (AIEd) that depends on advanced computing technologies, AI performance prediction model is widely used to identify at-risk students that tend to fail, establish student-centered learning pathways, and optimize instructional design and development. A majority of the existing AI prediction models focus on the development and optimization of the accuracy of AI algorithms rather than applying AI models to provide student with in-time and continuous feedback and improve the students’ learning quality. To fill this gap, this research integrated an AI performance prediction model with learning analytics approaches with a goal to improve student learning effects in a collaborative learning context. Quasi-experimental research was conducted in an online engineering course to examine the differences of students’ collaborative learning effect with and without the support of the integrated approach. Results showed that the integrated approach increased student engagement, improved collaborative learning performances, and strengthen student satisfactions about learning. This research made contributions to proposing an integrated approach of AI models and learning analytics (LA) feedback and providing paradigmatic implications for future development of AI-driven learning analytics.

【 授权许可】

CC BY   
© The Author(s) 2023

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