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- Iranian Distance Education is an open access and peer-reviewed bi-quarterly journal dedicated to the advancement of Iranian Distance Education Papers are subject to a double-bli... moreIranian Distance Education is an open access and peer-reviewed bi-quarterly journal dedicated to the advancement of Iranian Distance Education Papers are subject to a double-blind peer review process to ensure the quality of their underlying research methodology and argument. The submitted papers will be published after review as well as the approval of the editorial board. The journal was established by Payame Noor University. The honorable professors and researchers are highly appreciated if they visit this site, register, submit and set up their papers based on authors guidelines. Therefore, visiting in person or calling the journal office are not recommended, so all connections with authors and reviewers are done through the website. The journal provides article download statistics.edit
Massive open online courses (MOOCs) have recently becoming a popular means of education. They generally give the students large-scale options. However, the diversity of MOOC courses available and their rapid updates make it more difficult... more
Massive open online courses (MOOCs) have recently becoming a popular means of education. They generally give the students large-scale options. However, the diversity of MOOC courses available and their rapid updates make it more difficult for students to find fresh material relevant to them. A recommendation system (RS) connects the learner with the best learning resources to meet students' interests. The majority of recommender system research is based on the existence of explicit feedback, which is often impossible or inaccessible in MOOCs. As a result, in this paper, we model user positive and negative preferences using implicit feedback acquired passively by watching various types of students' behavior. This paper proposes a novel course recommendation, which employs Siamese Neural Networks (SNNs) to extract latent representations of students and courses using a loss function that favors observed over unobserved courses. The similarity of users and courses is then determined using a novel representation mechansim.