The Exploitation of Traces Serving Tutors for the Reconstruction of Groups within a CBLE

Salihoun, Mohammed; Guerouate, Fatima; Sbihi, Mohamed

doi:10.1016/j.sbspro.2014.09.184

Cited by 5 publications

(3 citation statements)

References 2 publications

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“…The second, considers the learner in a group (Tadlaoui, 2018;Tadlaoui et al, 2015, Ghenname, 2015; it can be defined as a social recommendation approach. Studies have shown that being isolated makes it difficult for a learner to progress in the learning process, which is why there has been much interest in integrating the learner into a group through collective activities and exchanges (Salihoun et al, 2014). Examples include peer evaluation (Bouzidi and Jaillet, 2007), and the integration of social media modules into learning environments (facebook, linkedin) (Tadlaoui et al, 2015, Ghenname, 2015.…”

Section: Background Of Our Workmentioning

confidence: 99%

Hybrid Educational Recommendation System: Experimentation With a Voting-Based Evaluation Mode

2020

IJWI

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Section: Background Of Our Workmentioning

confidence: 99%

Hybrid Educational Recommendation System: Experimentation With a Voting-Based Evaluation Mode

2020

IJWI

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“…However, these methods use a high level of abstraction, which allows them to be adapted to a wide variety of different problems [25].…”

Section: Idea and Algorithmmentioning

confidence: 99%

How to Assist Tutors to Rebuild Groups Within an ITS by Exploiting Traces. Case of a Closed Forum.

Salihoun¹,

Guerouate²,

Berbiche³

et al. 2017

Int. J. Emerg. Technol. Learn.

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Abstract-Computer Supported Collaborative Learning (CSCL) is a new mode of teaching and one of the popular approaches for learning process. It allows virtual interactions between groups by providing tools such as: chat, internal email and discussion forums. One of the major problems caused by this learning process is the neglect and isolation of learners in groups, and usually is the cause of a heterogeneous group through social, cognitive or emotional ways. The method used is based on the exploitation of traces left on the online learning platform by learners and groups. The data collected from the environment can be observed and exploited in order to build social and cognitive indicators. Our approach is to design a model which assists the tutor to rebuild groups who are not homogeneous in order to prevent their isolation and abandonment. Our model offers the tutor the opportunity to rebuild the groups in an automatic way and based on the characteristics of quantitative indicators of all learners. Our work allowed us to test our algorithm from a functional and technical point of view and also identifies real variables from a collaborative online learning. It also allowed us to evaluate six different indicators proposed for this experiment, showing that they may assist the tutor to rebuild many groups again. The results show us that after the rebuilding groups, there has been a lot of participation in the forum and a considerable number of shares and documents deposited to the forum for each group. This high frequency of interaction between learners, lead them to a fruitful collaboration, and a good quality work at the end.

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“…Recommendation systems (RSs) have been used and adapted in education as a means of offering each learner the educational resources best suited to his or her profile or needs, as is done in the field of e-commerce (Burke et al, 2011). According to the filtering methods, the RSs are based upon two essential techniques: the first focused on the specificities of the user (Herath & Jayarathne, 2018;Leblay, 2016), and the second focused on the preferences and tendencies of the group to which the user belongs (Salihoun et al, 2014;Tadlaoui et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Hybrid Filtering Recommendation System in an Educational Context

Baidada

Mansouri

Poirier

2021

International Journal of Web-Based Learning and Teaching Technologies

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In education, the needs of learners are different in the majority of the time, as each has specificities in terms of preferences, performance and goals. Recommendation systems have proven to be an effective way to ensure this learning personalization. Already used and tested in other areas such as e-commerce, their adaptation to the educational context has led to several research studies that have tried to find the best approaches with the best expected results. This article suggests that a hybridization of recommendation systems filtering methods can improve the quality of recommendations. An experiment was conducted to test an approach that combines content-based filtering and collaborative filtering. The results proved to be convincing.

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The Exploitation of Traces Serving Tutors for the Reconstruction of Groups within a CBLE

Cited by 5 publications

References 2 publications

Hybrid Educational Recommendation System: Experimentation With a Voting-Based Evaluation Mode

Hybrid Educational Recommendation System: Experimentation With a Voting-Based Evaluation Mode

How to Assist Tutors to Rebuild Groups Within an ITS by Exploiting Traces. Case of a Closed Forum.

Hybrid Filtering Recommendation System in an Educational Context

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