Learning Management Systems (LMSs) have been widely employed following the Covid-19 pandemic. The user modeling of LMS including educators and learners is a point of interest for Higher Education Institutions (HEI), stakeholders and system users. In this work user’s engagement with LMS is modeled using the Quality of Interaction (QoI) indicator under a combined approach of blended and collaborative learning. The present research extends the previous work of ‘Fuzzy QoI’ and ‘DeepLMS’ to develop a generalized model that substitutes the fuzzy logic system with a deep learning model. In this line, Temporal Convolutional Neural Networks (T-CNN) were used to predict QoI, achieving MAE (0.027), RMSE (0.066) and R2 (0.698). The feedback received from the T-CNN model provides insights to educators and stakeholders in order to enhance the pedagogical experience.
CITATION STYLE
Awad, A., AlShehhi, A., Dias, S. B., Hadjileontiadis, S. J., & Hadjileontiadis, L. J. (2022). On Modeling LMS Users’ Quality of Interaction Using Temporal Convolutional Neural Networks. In Communications in Computer and Information Science (Vol. 1720 CCIS, pp. 145–154). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-22918-3_11
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