A social learning analytics approach to cognitive apprenticeship

Eman Abu Khousa, Yacine Atif, Mohammad M. Masud

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


The need for graduates who are immediately prepared for employment has been widely advocated over the last decade to narrow the notorious gap between industry and higher education. Current instructional methods in formal higher education claim to deliver career-ready graduates, yet industry managers argue their imminent workforce needs are not completely met. From the candidates view, formal academic path is well defined through standard curricula, but their career path and supporting professional competencies are not confidently asserted. In this paper, we adopt a data analytics approach combined with contemporary social computing techniques to measure, instil, and track the development of professional competences of learners in higher education. We propose to augment higher-education systems with a virtual learning environment made-up of three major successive layers: (1) career readiness, to assert general professional dispositions, (2) career prediction to identify and nurture confidence in a targeted domain of employment, and (3) a career development process to raise the skills that are relevant to the predicted profession. We analyze self-declared career readiness data as well as standard individual learner profiles which include career interests and domain-related qualifications. Using these combinations of data sources, we categorize learners into Communities of Practice (CoPs), within which learners thrive collaboratively to build further their career readiness and assert their professional confidence. Towards these perspectives, we use a judicious clustering algorithm that utilizes a fuzzy-logic objective function which addresses issues pertaining to overlapping domains of career interests. Our proposed Fuzzy Pairwise-constraints K-Means (FCKM) algorithm is validated empirically using a two-dimensional synthetic dataset. The experimental results show improved performance of our clustering approach compared to baseline methods.

Original languageEnglish
Article number14
JournalSmart Learning Environments
Issue number1
Publication statusPublished - Dec 2015


  • Big data
  • Career readiness
  • Clustering
  • Community of practice
  • Computational science
  • Fuzzy logic
  • Learning analytics
  • Social networks

ASJC Scopus subject areas

  • Education
  • Computer Science Applications


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