From Learner Information Packages to Student Models: Which Continuum?

Oubahssi, Lahcen; Grandbastien, Monique

doi:10.1007/11774303_29

Cited by 9 publications

(10 citation statements)

References 1 publication

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“…3) Enumerate important terms in the ontology: This step, as its name indicates, makes it possible to list the terms related to the ontology (terms that we would discuss about and their properties, the relationship between them, etc.). To create a useful list, we started by writing terms related to learner modelling, we scanned articles on learner modelling [3,10,12,44,45], and on mental disorders that are relevant to our research [27,36,38,46] which we discussed in the second part. In addition to the various glossaries.…”

Section: A Processmentioning

confidence: 99%

Towards an Ontological Learner’s Modeling During and After the COVID-19 Pandemic

Ouatiq¹,

Guemmat²,

Mansouri³

et al. 2021

IJACSA

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The health crisis and the unprecedented upheaval in the education systems which it caused are far from being over, consequently, the adaptation of the learning experience is most needed, and it should take into consideration the criteria of this specific crisis and its impact on the physical and mental health of the learners. In this article, we aimed to present an ontologybased learner model that will bring together the pedagogical and psychological characteristics, but also the health risks generated by the epidemic on the learners, following a Knowledge-Engineering Methodology. We developed an ontology that combines the IMS-LIP standard features and the learner characteristic. It is ready for different uses in different systems and situations during and after the COVID-19 pandemic, and it will give a global representation of the learner in order to allow him to get the best-adapted courses.

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Section: A Processmentioning

confidence: 99%

Towards an Ontological Learner’s Modeling During and After the COVID-19 Pandemic

Ouatiq¹,

Guemmat²,

Mansouri³

et al. 2021

IJACSA

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“…Moreover, the IMS RDCEO standard involves improved elements of existing categories in the IMS LIP standard and other new categories namely profile, educational path, metadata, and comment (Oubahssi and Grandbastien, 2006). The profile category includes a set of information gathered as an output of a learning unit.…”

Section: Datasetsmentioning

confidence: 99%

Semi-supervised algorithm with knowledge-based features for learner's profiles interoperability

Ghorbel

Zayani

Amous

et al. 2018

IJTEL

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Nowadays, the user can have several profiles found in different adaptive systems relative to various fields. In particular, adaptive e-learning systems respond to a strong need to adapt to each learner their proposed activities based on the data stored in his/her profile (learning-style, interest, etc.). However, each system can have incomplete data as far as the learner is concerned. Hence, the exchange of the learner's profile data is extremely important in order to enhance his/her learning experience. The exchange requires a matching process so as to resolve the large number of a learner's profiles differences whether in syntax, structure or semantics. In this context, we propose a matching process to automatically detect the similarity between the profile elements. The originality of this process resides in the fact that it rests on a new semi-supervised Tri-Training algorithm which significantly improves the state of the art approaches.

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“…From this perspective, it seems necessary to create an assessor model that naturally inherits models and standards of learners modeling such as the "PAPI Learner" standard [33], or IMS LIP (Information Management System -Learning Information Package) [34] or the model proposed by Battou in the context of the ALS-CPL project.…”

Section: Assessor Modelmentioning

confidence: 99%

Involvement of Learners’ Characteristics within the Allocation of Submissions in the context of Peer Assessment in MOOCs

Abrache¹,

Qazdar²,

Cherkaoui³

2017

IJCA

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The contribution in the context of this paper relates to the stage of allocating submissions to the assessors within the peer assessment process in MOOCs. We propose an algorithm for the distribution of assignments that involves the learner characteristics related to the evaluation in the methodology of allocating assignments. The inputs of this algorithm are the assessment profiles of learners which include their basic characteristics linked to the evaluation process. The creation of these profiles stands on the use of an assessor model inspired from the literature on learners modeling, a model that we are also discussing in this paper.

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From Learner Information Packages to Student Models: Which Continuum?

Cited by 9 publications

References 1 publication

Towards an Ontological Learner’s Modeling During and After the COVID-19 Pandemic

Towards an Ontological Learner’s Modeling During and After the COVID-19 Pandemic

Semi-supervised algorithm with knowledge-based features for learner's profiles interoperability

Involvement of Learners’ Characteristics within the Allocation of Submissions in the context of Peer Assessment in MOOCs

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