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

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 d...