Finding optimal pedagogical content in an adaptive e-learning platform using a new recommendation approach and reinforce
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ORIGINAL RESEARCH
Finding optimal pedagogical content in an adaptive e‑learning platform using a new recommendation approach and reinforcement learning Youness Madani1 · Hanane Ezzikouri1 · Mohammed Erritali1 · Badr Hssina2 Received: 24 July 2019 / Accepted: 30 October 2019 © Springer-Verlag GmbH Germany, part of Springer Nature 2019
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. Keywords E-learning · Collaborative filtering · Social filtering · Semantic similarity · Fuzzy logic · Reinforcement learning
1 Introduction With the exponential growth of the population that want to learn online, the e-learning platforms need to adapt and innovate in the way they suggest courses to learners. In the literature, we find a lot of methods and approaches that try to find optimal courses to a learner like those based on algorithms such as genetic algorithm, or the use of the machine learning approaches. In recent years companies and researchers begin to use the basis of recommendation systems in e-learning. In an e-learning platform, there are different types of learners. For example, we can find learners that prefer learning through tutorials, other learners can prefer using videos for learning new courses, and others can choose using questions/answers for reaching their goals. The difference can be shown also in the motivation of learners, in their skills, and * Youness Madani [email protected] 1
Faculty of Sciences and Technics, Sultan Moulay Slimane University, Beni Mellal, Morocco
Faculty of Science and Technics, Hassan II University of Casablanca, Mohammedia, Morocco
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also in their ability to master new concepts. With this difference between learners, the e-learning platforms must find new optimal approaches to take into account all the learners’ preferences, and also for augmenting the quality of learning. Recommender systems (RS) help people to find products and services which much their preferences and needs (Jang et al. 2019) and to reduce the amount of time they spend to find the items they are looking for. They are becoming increasingly important in a range of applications, su
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