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Cited by 108Online Learning and AnalyticsEducational Technology in LearningOnline and Blended Learning

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

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Liao, Chen-Hsuan, Wu, Jiun-Yu·2022·Computers & Education
DOI 10.1016/j.compedu.2022.104599
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Abstract

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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Metadata

Authors
Liao, C.-H., & Wu, J.-Y.
Affiliations
National Yang Ming Chiao Tung University (TW)
Corresponding author
Wu, Jiun-Yu
ORCID
Venue
Computers & Education
Publisher
Elsevier BV
Published
2022-12
Volume
190
Pages
104599
ISSN
0360-1315 (ISSN-L)
DOI
10.1016/j.compedu.2022.104599
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
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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