Literaturnachweis - Detailanzeige
Autor/inn/en | Ouyang, Fan; Wu, Mian; Zheng, Luyi; Zhang, Liyin; Jiao, Pengcheng |
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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 |
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 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). |
Anmerkungen | BioMed Central, Ltd. Available from: Springer Nature. 233 Spring Street, New York, NY 10013. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-348-4505; e-mail: customerservice@springernature.com; Web site: https://www.springer.com/gp/biomedical-sciences |
Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
Update | 2024/1/01 |