Authors:
Sahar Sayahi
1
;
Leila Ghorbel
1
;
Corinne Zayani
1
and
Ronan Champagnat
2
Affiliations:
1
MIRACL Laboratory, Sfax University, Tunis Road Km 10 BP.242, Sfax, 3021, Tunisia
;
2
L3i Laboratory, La Rochelle University, Avenue Michel Crépeau, La Rochelle, 17042, France
Keyword(s):
Online Learning, Recommender System, Filter Bubble, Educational Dataset, Data Mining, Serendipity Dimensions.
Abstract:
Since the outbreak of the pandemic, online learning has become widely applied. Indeed, learners follow Learning Resources (LR) available on different platforms. Therefore, it’s very difficult for learners to choose LR that matches their needs. They may face disorientation and cognitive overload problems. In fact, multiple studies have been conducted on Recommender Systems (RS) in order to provide learners with the best LR that correspond to their needs and complete their training. Unfortunately, these basic RS can lead to an overly restricted set of suggestions and inadvertently place learners in a so-called “filter bubble”. The latter is resolved through serendipitous RS, which suggest to learners surprising LR based on serendipity dimensions such as unexpectedness, novelty and usefulness. In this research paper, we first present our serendipity-oriented recommendation architecture. Then, we enrich our collected educational dataset with the dimensions of serendipity. Finally, we eva
luate the real learner’s satisfaction on serendipitous LR’s recommendation.
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