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

• Published in: International Journal of Educational Technology in Higher Education Vol. 20; no. 1; pp. 4 - 23
• Main Authors: Ouyang, Fan, Wu, Mian, Zheng, Luyi, Zhang, Liyin, Jiao, Pengcheng
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.01.2023; BioMed Central, Ltd; Springer Nature B.V; SpringerOpen
• Subjects: Academic Achievement; Academic performance prediction; AI prediction models; Algorithms; Artificial Intelligence; Artificial intelligence in education (AIEd); At Risk Students; Closed loops; Collaborative learning; Computer Appl. in Social and Behavioral Sciences; Computer Science; Computers and Education; Cooperative Learning; Design optimization; Distance learning; Educational Improvement; Educational Technology; Engineering Education; Feedback; Higher Education; Humanities; Information Systems Applications (incl.Internet); Instructional Design; Integrated approach; Law; Learner Engagement; Learning; Learning Analytics; Mathematical analysis; Online Courses; Online higher education; Performance prediction; Prediction; Prediction models; Research Article; Statistics for Social Sciences; Student Centered Learning; Student Improvement; Student participation; Student Satisfaction; Students; Technology Integration; Artificial intelligence in education (AIEd); Academic performance prediction; Collaborative learning; AI prediction models; Online higher education
• ISSN: 2365-9440; 2365-9440
• DOI: 10.1186/s41239-022-00372-4

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. Highlights Integrated approach was used to combine AI with learning analytics (LA) feedback Quasi-experiment research was conducted to investigate student learning effects Integrated approach to foster student engagement, performances and satisfactions Paradigmatic implication was proposed for develop AI-driven learning analytics Closed loop was established for both AI model development and educational application.