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

• Autores: Fan Ouyang, Mian Wu, Luyi Zheng, Liyin Zhang, Pengcheng Jiao
• Localización: International Journal of Educational Technology in Higher Education, ISSN 2365-9440, Nº. 20, 2023
• Idioma: inglés
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• Resumen

  • As a cutting-edge feld of artifcial 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 fll this gap, this research integrated an AI performance prediction model with learning analytics approaches with a goal to improve student learning efects in a collaborative learning context. Quasiexperimental research was conducted in an online engineering course to examine the diferences of students’ collaborative learning efect 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.