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

Abstract

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.