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Cited by 387Online Learning and AnalyticsIntelligent Tutoring Systems and Adaptive LearningArtificial Intelligence in Education

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

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Ouyang, Fan, Wu, Mian, Zheng, Luyi, Zhang, Liyin, Jiao, Pengcheng·2023·International Journal of Educational Technology in Higher Education
DOI 10.1186/s41239-022-00372-4PMC PMC9842403
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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.

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  • Studies that integrated LMS data with surveys and interviews uncovered deeper patterns and underlying causes of student behaviours.

    “For instance, studies that integrated LMS data with surveys and interviews uncovered deeper patterns and underlying causes of student behaviours (Dobashi et al., 2022;Ouyang et al., 2023).”

    From Unpacking student engagement in higher education learning analytics: a systematic review (2024) · cites this paper as Ouyang et al., 2023

    The Data collection and analysis section (page unavailable) describes combining DingTalk discussions, written assignments, and students’ personal reflections; the Results section identifies patterns in engagement and perceptions. It does not describe integrating LMS data with surveys and interviews, or establish underlying causes of student behaviour. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Ouyang, F., Wu, M., Zheng, L., Zhang, L., & Jiao, P.
Affiliations
Zhejiang University (CN)
Corresponding author
Jiao, Pengcheng
ORCID
Venue
International Journal of Educational Technology in Higher Education
Publisher
Springer Science and Business Media LLC
Published
2023-01-17
Volume
20
Issue
1
Pages
4-4
ISSN
2365-9440 (ISSN-L)
DOI
10.1186/s41239-022-00372-4
PMC
PMC9842403
OpenAlex
W4316657447
Cited by
387
References
5
Citations by year
2026: 114 · 2025: 133 · 2024: 104 · 2023: 36
FWCI
87.94
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Educational Technology in Higher Education (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Natural Science Foundation of China (62177041)
  • the Graduate Education Research Project of Zhejiang University (20210308)
  • Universitat Oberta de Catalunya
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
Keywords
learning analytics, cooperative learning, student engagement, at-risk students, student satisfaction, learning outcomes

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