A lightweight and reliable framework toward real-time student engagement predictions in learning analytics

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dc.contributor.authorHoang, Longen
dc.contributor.authorShorten, Georgeen
dc.contributor.authorO'Sullivan, Barryen
dc.contributor.authorNguyen, Hoang D.en
dc.contributor.funderResearch Irelanden
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2025-04-22T08:30:36Z
dc.date.available2025-04-22T08:30:36Z
dc.date.issued2024en
dc.description.abstractLearning analytics can enable the provision of meaningful feedback based on the collected data, help educators to make decisions with and about learners, and improve learner performance. Student engagement predictions are a key factor in generating feedback for real-time learning analytics applications, such as dashboards. However, most previous work has been based on a heavy deep learning model, which results in challenges for deployment in real-time applications (a resource efficiency requirement in reliable AI). This paper proposes a lightweight deep-learning framework for predicting student engagement in video to address this limitation. The proposed method uses customized MobileNetV2 as the backbone, with an input size of 32 by 32 by 3, to extract features from consecutive video frames. Multi-Scale attention – Residual (MUSER) is used to capture global information and contextual representation of the extracted features. Finally, LSTM examines the temporal variations in video frames and yields the prediction result. We use the DAISEE dataset, the most popular dataset in the learning analytics community, to evaluate the proposed framework. Experimental results demonstrate that the proposed method achieves good accuracy while significantly reducing the model size compared to other approaches.en
dc.description.sponsorshipResearch Ireland (12/RC/2289-P2)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationHoang, L., Shorten, G., O'Sullivan, B. and Nguyen, H. D. (2024) 'A lightweight and reliable framework toward real-time student engagement predictions in learning analytics', Reliable and Trustworthy Artificial Intelligence Workshop at the 16th Asian Conference on Machine Learning (ACML 2024), Hanoi, Vietnam, 5-8 December 2024.en
dc.identifier.endpage10en
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/17289
dc.language.isoenen
dc.relation.ispartofReliable and Trustworthy Artificial Intelligence Workshop at the 16th Asian Conference on Machine Learning (ACML 2024), Hanoi, Vietnam, 5-8 December 2024.en
dc.relation.project12/RC/2289-P2en
dc.rights© 2024, the Authors. For the purpose of Open Access, the authors have applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectLearning analyticsen
dc.subjectFeedbacken
dc.subjectLightweight deep learning frameworken
dc.subjectStudent engagement predictionsen
dc.subjectReliable AIen
dc.titleA lightweight and reliable framework toward real-time student engagement predictions in learning analyticsen
dc.typeConference itemen
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