Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course
By Ouyang Fan, Mian Wu, Luyi Zheng, Liyin Zhang, Pengcheng Jiao. International Journal of Educational Technology in Higher Education, 2023
301 citations recorded by the source corpus.
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. 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.
Subfields: Computer Science Applications, Health Informatics
View Paper: https://www.semanticscholar.org/paper/02cbb1591e65713f848bcb8a385b60a57b1bee93
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In Health Informatics
Citations in the corpus
- Artificial intelligence in intelligent tutoring systems toward sustainable education: a systematic review (2023, methodology): “Here, we combine performance prediction and behavior analysis because normally learning behaviors are captured as one of the features for performance prediction (Ouyang et al., 2023; Weng et al., 2020).”