A K-complementarity Technique for Forming Groups of Tutors in Intelligent Learning Environments

Lafifi, Yacine; Bendjebar, Safia; Zedadra, Amina

doi:10.2498/cit.1002377

Cited by 5 publications

(2 citation statements)

References 36 publications

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“…-Learner-Tutor Grouping System: It contains two grouping algorithms: Complement Algorithm and Incremental Complementarity Algorithm [4]. These algorithms are used to group the tutors, but their absence for technical or family reasons may influence their actions towards their learners.…”

Section: Functional Architecture Of the Proposed Systemmentioning

confidence: 99%

“…In these situations, the obvious strategy, then, is to propose to offer learners another dimension in their tutoring model by assigning the task of tutoring to the learners so becoming learner-tutor. This space offers all the tools and mechanisms to support and promote collaboration between the learner tutors in order to solve the learners' problems then to propose a new method of clustering learners in CEHL and this is based on the work of Y. Lafifi [4]. The aim of this method is to combine the tw o algorithms (complement algorithm and complementarity algorithm) that aims at grouping learners.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A New Method for Modeling Learners in a Collaborative Human Learning Environment

Seddari

Kerraoui

Drissi

2022

Advanced Intelligent Systems for Sustainable Development (AI2SD’2020)

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Computer Environments for Human Learning (CEHL) offers several pedagogical activities to the learners such as: learning, self-assessment, communication with other learners and the problem solving, etc. this latters can benefit from these services at any time and from any place. But, the learner's isolation and the lack of a support service have a negative impact on his learning, his achievements, and especially on his motivation during his educational activities. So, to cop this, tutoring is required in CEHL. It can be defined as a social relationship between an expert person called tutor and a person in difficulty, the learner. Since, tutoring is often the ideal solution for learners; this is why several research works were introduced in recent years. However, Most of them have found several difficulties concerning the tutor's work. This paper presents a novel arch to model learners in a collaborative human learning environment. The aim is to propose a method for the Modeling of learner-tutor. Moreover, we based on the learner's response to a questionnaire in order to create his or her own profile. This space offers all the tools and mechanisms to support and promote collaboration between learnertutors in this way the problems of learners is solved.

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Section: Functional Architecture Of the Proposed Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Method for Modeling Learners in a Collaborative Human Learning Environment

Seddari

Kerraoui

Drissi

2022

Advanced Intelligent Systems for Sustainable Development (AI2SD’2020)

View full text Add to dashboard Cite

show abstract

The Development and Implementation of Competency-Based ITS for Learners with Learning Disability

Bisht

Ahuja

2021

Advances in Intelligent Systems and Computing

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Collaborative Calibrated Peer Assessment in Massive Open Online Courses

Boudria

Lafifi

Bordjiba

2020

Learning and Performance Assessment

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The free nature and open access courses in the Massive Open Online Courses (MOOC) allow the facilities of disseminating information for a large number of participants. However, the “massive” propriety can generate many pedagogical problems, such as the assessment of learners, which is considered as the major difficulty facing in the MOOC. In fact, the immense number of learners who exceeded in some MOOC the hundreds of thousands make the instructors' evaluation of students' production quite impossible. In this work, the authors present a new approach for assessing the learners' production in MOOC. This approach combines the peer assessment with the collaborative learning and the calibrated method. It aims at increasing the degree of trust in peer-assessment. For evaluating the proposed approach, the authors implemented a MOOC dedicated for learning algorithms. In addition, an experiment was conducted during two months for knowing the effects of the proposed approach. The obtained results are presented in this paper. They are judged as very interesting and encouraging.

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A K-complementarity Technique for Forming Groups of Tutors in Intelligent Learning Environments

Cited by 5 publications

References 36 publications

A New Method for Modeling Learners in a Collaborative Human Learning Environment

A New Method for Modeling Learners in a Collaborative Human Learning Environment

The Development and Implementation of Competency-Based ITS for Learners with Learning Disability

Collaborative Calibrated Peer Assessment in Massive Open Online Courses

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