A lightweight and reliable framework toward real-time student engagement predictions in learning analytics
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Accepted Version
Date
2024
Authors
Hoang, Long
Shorten, George
O'Sullivan, Barry
Nguyen, Hoang D.
Journal Title
Journal ISSN
Volume Title
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Published Version
Abstract
Learning 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.
Description
Keywords
Learning analytics , Feedback , Lightweight deep learning framework , Student engagement predictions , Reliable AI
Citation
Hoang, 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.