Abstract
Students’ assessment based on score is no longer relevant today, this is after the major changes of students’ way of life and how they receive knowledge, especially after being immersed in social medias and modern technologies. In light of these significant changes, new methods of students’ assessment must be adopted in order to respond to the requirements of students of the 21st century, and to provide a real evaluation that reflects their knowledge, performance, and skills. Therefore, the Competency-Based Assessment might be a good candidate to meet these requirements. The scope of this paper is to address the issue of competency modelling in technology-enhanced learning systems in order to discover implicit competencies hidden behind students’ activities and how to translate them into acquired competencies. To face these challenges, the authors proposed an approach of semantic analytics of students’ activities data. Therefore, they modelled all knowledge about students and their competencies by Semantic Web and ontological representation; then students’ models have been subjected to a set of learning analytics approaches in order to analyze and evaluate the generated data according to the assessment model. An experimental study indicates that this approach is efficient and expected to show great advantages in evaluating students’ competencies.
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Acknowledgement
This article was presented with funding of CMEP Tassili project “Adaptation des parcours d’apprentissage des apprenants en utilisant les techniques de l’IA pour l’introduction de la pensée informatique à l’école”.
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Halimi, K., Seridi-Bouchelaghem, H. (2020). Where the Competency-Based Assessment Meets the Semantic Learning Analytics. In: Kumar, V., Troussas, C. (eds) Intelligent Tutoring Systems. ITS 2020. Lecture Notes in Computer Science(), vol 12149. Springer, Cham. https://doi.org/10.1007/978-3-030-49663-0_35
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DOI: https://doi.org/10.1007/978-3-030-49663-0_35
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