Students’ competencies discovery and assessment using learning analytics and semantic web




competency-based assessment, semantic web, technology enhanced learning, personalisation and profiling, artificial intelligence, qualitative survey


Traditional content-based assessment systems, which depend on the score as a key criterion for students’ evaluation, have proven to have many drawbacks, especially with the development of learning methods in recent years. Based on these developments, there is a need to adopt new assessment methods to assess the actual skills of students in the digital age. Therefore, the competency-based assessment approach is adopted in this paper to address the subject of students’ competency modelling and discovery in technology-enhanced learning systems. This method of assessment is perfectly suited to modern teaching trends. The authors proposed an approach of assessment semantic analytics to be used for discovering and assessing students’ competencies. This study notes that, all knowledge about students and their competencies has been modelled by semantic representations. Student’s models have been subjected to a set of learning analytics approaches to analyse data generated by students’ activities in order to discover their explicit and latent competencies hidden behind their activities. This experimental study indicates that the competency-based assessment approach is efficient and expected to show significant advantages in evaluating students’ competencies.

Implications for practice or policy:

  • Students become able to organise their gains, then integrate and employ them in solving life's problems.
  • Educators get to know more about the extent to which the objectives of their educational process are achieved by evaluating the intellectual, cultural, knowledge, and skilful assets that the learners obtain.
  • Educational policymakers can have a pedagogical and technical vision to move from the culture of content-based evaluations to a competency-based assessment.


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Author Biography

Khaled Halimi, Guelma University

Khaled Halimi is currently working as teacher of computing at the Computer Science Department of Guelma University (Algeria) and he is a researcher at the LabSTIC laboratory. He holds the PhD degree at Annaba University. His research fields are Collaborative Learning, Social Semantic Web and Social Networks.




How to Cite

Halimi, K., & Seridi-Bouchelaghem, H. (2021). Students’ competencies discovery and assessment using learning analytics and semantic web. Australasian Journal of Educational Technology, 37(5), 77–97.



Special Issue 2021 - Emerging Technologies - Innovative Ped/Competency Develop