Abstract
In the learning process, learners have different skills and each one has his own knowledge and his own ability to learn. The adaptive e-learning platforms try to find optimal courses for learners based on their knowledge and skills. Learning online using e-learning platforms becomes indispensable in the teaching process. Companies and scientific researchers try to find new optimal methods and approaches that can improve education online. In this paper, we propose a new recommendation approach for recommending relevant courses to learners. The proposed method is based on social filtering(using the notions of sentiment analysis) and collaborative filtering for defining the best way in which the learner must learn, and recommend courses that better much the learner’s profile and social content. Our work consists also in proposing a new reinforcement learning approach which helps a learner to find the optimal learning path that can improve the quality of learning.
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Notes
http://twitter4j.org. Twitter4J is an unofficial Java library for the Twitter API. With Twitter4J, you can easily integrate your Java application with the Twitter service. Twitter4J is an unofficial library.
https://opennlp.apache.org/. The Apache OpenNLP library is a machine learning-based toolkit for the processing of natural language text.
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Madani, Y., Ezzikouri, H., Erritali, M. et al. Finding optimal pedagogical content in an adaptive e-learning platform using a new recommendation approach and reinforcement learning. J Ambient Intell Human Comput 11, 3921–3936 (2020). https://doi.org/10.1007/s12652-019-01627-1
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DOI: https://doi.org/10.1007/s12652-019-01627-1