Abstract:

In today’s society, students and recent graduates are often struggling with finding a suitable job. Furthermore, worldwide population ageing, the constantly decreasing number of high school and university graduates, as well as the increasing number of dropouts, are causing a crisis on the labour market. In order to improve this alarming situation, young-aged students should be guided towards a suitable job, based on their preferences and skills. As career counsellors are being often understaffed or underused, technology can come up as a useful tool for creating a job recommendation system based on one’s personalised educational and career profile.

In order to check the feasibility of such a system, we conducted a survey including both high school and university students. The survey contained various sections related to the socio-demographics characteristics of the respondents, their criteria for choosing their educational or career path, as well as their familiarity with career path profiling or job recommendation platforms. The respondents were high-school pupils and first- and second-year students from Romanian public educational institutions.

Our current paper presents the quantitative and qualitative examination of the collected data from the survey’s responses and performs a detailed comparative analysis of the existing job recommendation tools (including here both traditional methods – questionnaires, face to face interviews with counsellors - and modern ones, relying on data mining algorithms, machine learning or ontologies). Based on the findings of the survey and the existing literature, we propose our own system entitled CareProfSys - Smart Career Profiler based on a Semantic Data Fusion Framework, which has the goal of helping both students and career counsellors in the job recommendation process. The system will extract data from various data sources (such as CVs, educational background and grades, social network profiles or psychological tests), process it in a structured form (an ontology) and provide recommendations using emergent technologies such as conversational agents and virtual reality.

Keywords:

Job recommendation, career assessment, educational counselling, ontology, data extraction / data mining.