Integration of Artificial Intelligence Performance Prediction and Learning Analytics to Improve Student Learning in Online Engineering Course

Autor/inn/en	Ouyang, Fan; Wu, Mian; Zheng, Luyi; Zhang, Liyin; Jiao, Pengcheng
Titel	Integration of Artificial Intelligence Performance Prediction and Learning Analytics to Improve Student Learning in Online Engineering Course
Quelle	In: International Journal of Educational Technology in Higher Education, 20 (2023), Artikel 4 (23 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
DOI	10.1186/s41239-022-00372-4
Schlagwörter	Technology Integration; Artificial Intelligence; Performance; Prediction; Learning Analytics; Educational Improvement; Academic Achievement; Online Courses; Engineering Education; Cooperative Learning; Instructional Design; Student Centered Learning; At Risk Students; Student Satisfaction; Models + Suchen Sie Ihr Suchwort? Künstliche Intelligenz; Achievement; Leistung; Vorhersage; Teaching improvement; Unterrichtsentwicklung; Schulleistung; Online course; Online-Kurs; Ingenieurausbildung; Kooperatives Lernen; Lesson concept; Lessonplan; Unterrichtsentwurf; Group work; Student-entered learning; Student-centred learning; Student centred learning; Schülerorientierter Unterricht; Schülerzentrierter Unterricht; Gruppenarbeit; Analogiemodell
Abstract	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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01