{'fraction_non_alphanumeric': 0.016564952048823016, 'fraction_numerical': 0.0, 'mean_word_length': 5.597701149425287, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Hybrid Knowlegde-Based Approach for Recommending Massive Learning Activities

In recent years, the development of recommender systems has attracted increased interest in several domains, especially in e-learning. Massive Open Online Courses have brought a revolution. However, deficiency in support and personalization in this context drive learners to lose their motivation and leave the learning process. To overcome this problem we focus on adapting learning activities to learners' needs using a recommender system.This paper attempts to provide an introduction to different recommender systems for e-learning settings, as well as to present our proposed recommender system for massive learning activities in order to provide learners with the suitable learning activities to follow the learning process and maintain their motivation. We propose a hybrid knowledge-based recommender system based on ontology for recommendation of e-learning activities to learners in the context of MOOCs. In the proposed recommendation approach, ontology is used to model and represent the knowledge about the domain model, learners and learning activities.