Journal Article

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

Zheng, Luyi, Jiao, Pengcheng, Ouyang, Fan, Zhang, Liyin, Wu, Mian

2023

Overview

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.HighlightsIntegrated approach was used to combine AI with learning analytics (LA) feedbackQuasi-experiment research was conducted to investigate student learning effectsIntegrated approach to foster student engagement, performances and satisfactionsParadigmatic implication was proposed for develop AI-driven learning analyticsClosed loop was established for both AI model development and educational application.

• Publisher: Springer Nature B.V
• Subject: Academic achievement / Algorithms / Artificial intelligence / At risk populations / At Risk Students / Closed loops / Collaboration / Collaborative learning / Cooperative learning / Design optimization / Distance learning / Education / Engineering / Engineering education / Feedback / Higher education / Instructional design / Integrated approach / Integrative approach / Learner Engagement / Learning / Learning analytics / Learning environment / Mathematical analysis / Optimization / Performance prediction / Prediction models / Quasi-experimental methods / Student Centered Learning / Student Improvement / Student participation / Students