Abstract
Massive open online courses (MOOCs) are used by universities and institutions offer valuable free courses to huge numbers of people around the world through MOOC platforms. However, because of the huge number of learners, they often not receive sufficient support from instructors and their peers during the learning process, leading to high dropout, low completion, and low success rates observed in the MOOCs. This chapter focuses on analysing relevant algorithms to develop a deep learning model to predict learner behaviour (learner interactions) in the learning process. For this analysis, we use data from UNESCO’s International Institute for Capacity Building in Africa MOOC platform designed for teacher training in Africa. We employed various geographical, social, and learning behavioural features to build deep learning models based on three types of recurrent neural networks (RNNs): simple RNNs, gated recurrent unit (GRU) RNNs, and long short-term memory (LSTM) RNNs. The models were trained using L2 regularization. Results showed that simple RNNs gave the best performance and accuracy on the dataset. We also observed correlations between video viewing, quiz behaviour, and the participation of the learner and conclude that we can use learner’s video or quiz viewing behaviour to predict their behaviour concerning other MOOC contents. We also observed that the shorter the video or quiz, the greater the number of viewers. These results suggest the need for deeper research on educational video and educational quiz design for MOOCs.
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Fotso, J.E.M., Batchakui, B., Nkambou, R., Okereke, G. (2022). Algorithms for the Development of Deep Learning Models for Classification and Prediction of Learner Behaviour in MOOCs. In: Alloghani, M., Thron, C., Subair, S. (eds) Artificial Intelligence for Data Science in Theory and Practice. Studies in Computational Intelligence, vol 1006. Springer, Cham. https://doi.org/10.1007/978-3-030-92245-0_3
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DOI: https://doi.org/10.1007/978-3-030-92245-0_3
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