TY - GEN
T1 - On Modeling LMS Users’ Quality of Interaction Using Temporal Convolutional Neural Networks
AU - Awad, Abdulrahman
AU - AlShehhi, Aamna
AU - Dias, Sofia B.
AU - Hadjileontiadis, Sofia J.
AU - Hadjileontiadis, Leontios J.
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Learning Management Systems (LMSs)
KW - Quality of Interaction (QoI)
KW - Temporal Convolutional Neural Networks (T-CNN)
UR - http://www.scopus.com/inward/record.url?scp=85148008075&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-22918-3_11
DO - 10.1007/978-3-031-22918-3_11
M3 - Conference contribution
AN - SCOPUS:85148008075
SN - 9783031229176
T3 - Communications in Computer and Information Science
SP - 145
EP - 154
BT - Technology and Innovation in Learning, Teaching and Education - 3rd International Conference, TECH-EDU 2022, Revised Selected Papers
A2 - Reis, Arsénio
A2 - Barroso, João
A2 - Martins, Paulo
A2 - Jimoyiannis, Athanassios
A2 - Huang, Ray Yueh-Min
A2 - Henriques, Roberto
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd International Conference on Technology and Innovation in Learning, Teaching and Education, TECH-EDU 2022
Y2 - 31 August 2022 through 2 September 2022
ER -