[ Approche instrumentée support de l'auto-évaluation des connaissances au service des formations de Master : perceptions, pratiques et pistes de propositions ]
Volume 6, Issue 3, July 2014, Pages 654–676
Ouidad LABOUIDYA1, Abdelhak AQQAL2, and Najib ELKAMOUN3
1 Laboratoire STIC - Département de Physique, Faculté des Sciences - Université Chouaib Doukkali, El Jadida, Morocco
2 Laboratoire de Technologie de l'Information, ENSA - Université Chouaib Doukkali, El Jadida, Morocco
3 Laboratoire STIC - Département de Physique, Faculté des Sciences - Université Chouaib Doukkali, El Jadida, Morocco
Original language: French
Copyright © 2014 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Any assessment is an objective driven process. In our case, the objective is to support training sessions by utilizing Technology-Enhanced approach, particularly ontologies and web semantic paradigms. Hence, we emphasize the idea that to provide a training which could fulfill individually the needs of all learners, we should establish an assessment of their prerequisites knowledge first. We call such method "Assessment Module". Such assessment is still a complex task and intellectually demanding for teachers. To suit our scenario of use and to meet the teacher' needs, we propose the modeling and the implementation of a new methodology for the learner's assessment in order to set-up an automated assessment for a better individualized training, particularly in Masters' training. Our approach is based on a conceptualization of knowledge and on a modeling of MCQ assessment added to a fuzzy-modeling method to refine the assessment's results. In this article, we present in detail our approach as well as two study cases we did as proof of concept, and the experimental results we got later on. The experimentations has validated our assumptions and has demonstrated that the framework we proposed and the way how it was designed provide a distinguished assessment of learners almost similar to when it is done manually by a human evaluator. Fulfilling this requirement is the prior step toward any Technology-Enhanced Individualization of training in higher education.
Author Keywords: Assessment, Modeling, Ontology, Decision processes, Teaching/Learning, Experiments.
Volume 6, Issue 3, July 2014, Pages 654–676
Ouidad LABOUIDYA1, Abdelhak AQQAL2, and Najib ELKAMOUN3
1 Laboratoire STIC - Département de Physique, Faculté des Sciences - Université Chouaib Doukkali, El Jadida, Morocco
2 Laboratoire de Technologie de l'Information, ENSA - Université Chouaib Doukkali, El Jadida, Morocco
3 Laboratoire STIC - Département de Physique, Faculté des Sciences - Université Chouaib Doukkali, El Jadida, Morocco
Original language: French
Copyright © 2014 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Any assessment is an objective driven process. In our case, the objective is to support training sessions by utilizing Technology-Enhanced approach, particularly ontologies and web semantic paradigms. Hence, we emphasize the idea that to provide a training which could fulfill individually the needs of all learners, we should establish an assessment of their prerequisites knowledge first. We call such method "Assessment Module". Such assessment is still a complex task and intellectually demanding for teachers. To suit our scenario of use and to meet the teacher' needs, we propose the modeling and the implementation of a new methodology for the learner's assessment in order to set-up an automated assessment for a better individualized training, particularly in Masters' training. Our approach is based on a conceptualization of knowledge and on a modeling of MCQ assessment added to a fuzzy-modeling method to refine the assessment's results. In this article, we present in detail our approach as well as two study cases we did as proof of concept, and the experimental results we got later on. The experimentations has validated our assumptions and has demonstrated that the framework we proposed and the way how it was designed provide a distinguished assessment of learners almost similar to when it is done manually by a human evaluator. Fulfilling this requirement is the prior step toward any Technology-Enhanced Individualization of training in higher education.
Author Keywords: Assessment, Modeling, Ontology, Decision processes, Teaching/Learning, Experiments.
Abstract: (french)
Nous proposons dans cet article l'élaboration et la mise en place d'une méthodologie d'instrumentalisation de l'auto-évaluation des connaissances de l'apprenant pour les systèmes éducatifs, particulièrement dans le cas des formations de Master. Notre approche consiste en la conceptualisation et la modélisation de connaissances et de QCM d'évaluation produits à base d'ontologies couplée avec un raisonnement flou. Nous présentons ensuite les résultats expérimentaux qui vont permettre de valider nos hypothèses de travail. L'originalité de cette contribution est qu'elle entretient l'expertise pédagogique de l'évaluation des acquis de l'apprenant le long de sa conception. Dans cette perspective, l'obtention d'information profitable sur les acquis de l'apprenant permet de mieux orienter l'apprentissage ainsi que les pratiques pédagogique dans l'enseignement supérieur.
Author Keywords: Evaluation, Modélisation, Ontologie, Processus de Décision, Enseignement/Apprentissage, Expériences.
How to Cite this Article
Ouidad LABOUIDYA, Abdelhak AQQAL, and Najib ELKAMOUN, “A Technology-Enhanced approach to automate assessments for Master's trainings: perceptions, practices and track proposals,” International Journal of Innovation and Applied Studies, vol. 6, no. 3, pp. 654–676, July 2014.
