Documents disponibles écrits par cet auteur

Toward a recommender system for helping learners at risk of dropping out in MOOCs / SEKKOU Amjad

Public ISBD Titre : Toward a recommender system for helping learners at risk of dropping out in MOOCs Type de document : projet fin études Auteurs : SEKKOU Amjad, Auteur Langues : Français (fre) Catégories : BIG DATA Index. décimale : mast 263/19 Résumé : Recently, Massive Open Online Courses (MOOCs) have aroused a great interest in the media. As the number of students enrolled in MOOCs increases, and given the lack of supervision, it becomes dicult for students to get answers to their questions through discussion forums, resulting a very high attrition rate. In this work, we addressed the problem of unanswered questions in the discussion forums, based on a combination of Social Network Analysis and Deep Learning. First, we analyzed a network that presents the interaction between students, and an other that illustrates the structure of the threads in order to obtain the lone questions. Then, we calculated a similarity score between these questions, using our proposed model. This last, is the key element of the semantic similarity approach between forum questions. The results of our experience on a Stanford University MOOC course show that our recommendation method has the potential to guide students to the answers of their questions, and also to learners who can help them. Thus, achieve the main goal of this master thesis: Reduce the high attrition rate in MOOCs. Toward a recommender system for helping learners at risk of dropping out in MOOCs [projet fin études] / SEKKOU Amjad, Auteur . - [s.d.]. Langues : Français (fre) Catégories : BIG DATA Index. décimale : mast 263/19 Résumé : Recently, Massive Open Online Courses (MOOCs) have aroused a great interest in the media. As the number of students enrolled in MOOCs increases, and given the lack of supervision, it becomes dicult for students to get answers to their questions through discussion forums, resulting a very high attrition rate. In this work, we addressed the problem of unanswered questions in the discussion forums, based on a combination of Social Network Analysis and Deep Learning. First, we analyzed a network that presents the interaction between students, and an other that illustrates the structure of the threads in order to obtain the lone questions. Then, we calculated a similarity score between these questions, using our proposed model. This last, is the key element of the semantic similarity approach between forum questions. The results of our experience on a Stanford University MOOC course show that our recommendation method has the potential to guide students to the answers of their questions, and also to learners who can help them. Thus, achieve the main goal of this master thesis: Reduce the high attrition rate in MOOCs.

Réservation

Réserver ce document

Exemplaires

Code barre	Cote	Support	Localisation	Section	Disponibilité
mast 263/19	mast 263/19 SEK	Texte imprimé	Unité des masters	Mast/19	Disponible