Bridging Education and Labor Skills by a Novel Competency-Based Course Linked-Data Model

• Main Authors: Safia Nahhas, Omaimah Bamasag, Maher Khemakhem, Nada Bajnaid
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Competency-based education; linked-data; OCW; ontology
• Online Access: https://ieeexplore.ieee.org/document/8811475/

Competency-based education (CBE) aligns with current requirements by providing students with competencies needed in real life. Emerging new styles of education, such as open courseware (OCW), massive open online course (MOOC) and open educational resources (OERs), play a role in renewing CBE by offering the required open materials. In this paper, we propose a novel competency-based course model (CBC model) that was developed with two aims. The first aim was to represent the fundamental concepts of CBE courses by exploiting the expressivity of ontologies. The second aim was to design a CBC model to act as a bridge between the existing ontologies that represent education and open materials and the ontologies that represent competencies from labor knowledge bases by exploiting linked-data interoperability. The model was designed to provide materials at the competency level in addition to the course level in order to allow students who fail one competency to repeat its materials rather than the whole course. Though the dynamic nature of labor knowledge bases, the integrated competencies from labors linked-data repositories will be automatically updated based on linked-data features. The use of W3C standards and linked-data ontology engineering principles to design the CBC model make it reusable and expandable. A proof of concept application was used to show the possibility of using the model in real life by applying the model to educational systems, such as LMS. Applying the model can increase a university's appearance on the web, thus enhancing its reputation. Additionally, the model can increase the links between education and labor that form a substantial contribution from the linked-data science perspective.