The DOI matches its Crossref record; 63 of the 66 references it cites were found in a registry or an index, and 1 are not checked yet.
Deploying multimodal learning analytics models to explore the impact of digital distraction and peer learning on student performance
Open accessAbstract
Social media have been extensively incorporated in higher education as an indispensable tool for learning. Nevertheless, research has conflicting findings about its effectiveness due to the highly reported digital distraction and poor peer learning engagement on social media. This study employed an innovative problem-based learning (PBL) pedagogy incorporating Learning Analytics to identify digital distraction, quantify the quality of peer learning engagement, and predict learning performance. Participants were 51 Taiwanese graduate students in blended Statistics courses under the PBL pedagogy. The multimodal Learning Analytics (LA) model contained data from learner discourse on Facebook groups and questionnaires, including learner characteristics, perceived digital distraction, subjective peer learning orientation, and objective peer learning engagement endorsed by machine learning (ML) models. Results showed that students reporting more digital distraction problems obtained lower final course grades, and those reporting stronger peer learning orientation received higher final course grades. Moreover, peer learning engagement objectively recognized by ML models has better predictive validity on academic performance than self-perceived peer learning orientation. The results of the multimodal LA models addressed the scarcity of studies involving learners' process data in PBL and informed instructional practices and strategies to scaffold students’ statistics learning upon detecting those at risk of distracted attention problems and poor peer learning engagement.
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“AI systems have the potential to collect and analyze student data, such as academic performance, learning preferences, and social interactions.This multi-modal data can provide personalized instruction leading to increased student engagement while improving academic performance (Liao & Wu, 2022).”
From The Future of Artificial Intelligence in Special Education Technology (2023) · cites this paper as Liao & Wu, 2022
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Metadata
- Authors
- Liao, C.-H., & Wu, J.-Y.
- Affiliations
- National Yang Ming Chiao Tung University (TW)
- Corresponding author
- Wu, Jiun-Yu
- Venue
- Computers & Education
- Publisher
- Elsevier BV
- Published
- 2022-12
- Volume
- 190
- Pages
- 104599
- ISSN
- 0360-1315 (ISSN-L)
- OpenAlex
- W4288969674
- Cited by
- 89
- References
- 66
- Citations by year
- 2026: 24 · 2025: 27 · 2024: 19 · 2023: 16 · 2022: 3
- FWCI
- 12.54
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Computers & Education (publishedVersion, cc-by-nc-nd)
- Funders
- National Science and Technology Council
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Education › Educational Technology in Learning
- Social Sciences › Education › Online and Blended Learning
- Keywords
- learning analytics, digital distraction, peer learning, problem-based learning, social media in higher education, Facebook groups, student performance prediction, graduate students, multimodal data, learner discourse, at-risk students
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