PROMA: AN ONTOLOGY BASED APPLICATION FOR EDUCATION/INDUSTRY COLLABORATION

In this research work we propose a Profile Matching Application (ProMa), a desktop and web application that allows the universities to highlight the gaps between their academic programs and the industry occupations. It provides a mean of communication between the different stakeholders to bridge the gap and adequately prepare the students for the market. The profile matching functionality is achieved through the capturing of all the learning outcomes of the university programs under evaluation and their mapping with the list of competencies required by the occupations in the industry (extracted from O*Net data base). The mapping is performed by a domain expert who, beside mapping the relevant competencies, specifies the level of relevance between the matched competencies which would be used by the system to calculate the matching score between the profiles.

Indeed, the profile matching score calculation involves calculating the matching score for each matched competency in the student profile or the university program. Then the total matching score is found by taking the average of the competency matching scores. The AHP (Analytic Hierarchy Process) method is used as an analysis tool to evaluate the level of matching against a set of three criteria: competency relevance, competency importance, and competency required level.

A RESTful API enables the user of the system to interact with the ontology and read the different aspects of the domain like university programs, occupation, etc.

The profile matching model is modelled as a semantic web ontology. The main parts of the ontology consist of the student and his/her transcript, the occupation and its requirements, the application which represents a student applying for an occupation, in addition to some classes which represent the mapping between the education world and the industrial world. The ontology is populated only with the necessary instances and derives as much information as possible using an inference engine. The ontology and its matching processes will be explained in more detail in the proposed full paper.

This work is part of the Qatar Fondation NPRP Pro-Skima research project, in partnership with Qatari and French HEIs.