PCRS: Personalized Career-Path Recommender System for Engineering Students

Abstract

Choosing a university specialization is a challenging decision for high-school students. Due to the lack of guidance and limited online resources, students base their decisions on subjective perceptions of family and friends. This increases the risk of high university dropout rates, and students changing their university disciplines. To address the aforementioned drawbacks, this research paper presents a Personalized Career-path Recommender System (PCRS) to provide guidance and help high school students choose engineering discipline. The design of PCRS is based on fuzzy intelligence of N-layered architecture and uses students' academic performance, personality type, and extra-curricular skills. The association between personality type and engineering discipline was built using a sample of 1250 engineering students enrolled in seven engineering disciplines at An-Najah National University. PCRS is implemented as a mobile application and it is tested against an evaluation sample of 177 engineers. The sample consists of graduate or undergraduate engineers who are satisfied with their engineering disciplines. The evaluation examined the agreement between the recommendations generated by PCRS and the 177 actual engineering discipline of the sample. The evaluation results proved a slight agreement between the suggested recommendations of PCRS and the actual discipline of the research sample. Hence, PCRS is capable of providing guidance to high-school students who are interested in pursuing their studies in Engineering. The PCRS application is the first career-path recommender to target Palestinian community and other developing countries in the MENA region. The design of PCRS is scalable and it can be expanded in the future to consider other academic majors of higher education.

Applying Data Mining in Graduates’ Employability

“…Author Techniques Knowledge Gap Bañeres and Conesa [37] Not clear from the literature Limited sample comprising a single university. Qamhieh, Sammaneh, and Demaidi [50] Fuzzy intelligence Proposed a recommender system for higher education in Palestine, but socio-economic drivers were not considered in the study Suryawanshi, Patil, and Choudhari [51] The study proposed to use ANN The study did not implement the recommender system. It proposed only a graphical framework Purkar, et al [52] Content based The model was limited to informal jobs.…”

“…Regarding reviewed prescriptive studies, all the papers insist on the use of skills as main factors that prescribe employability. However, Qamhieh et al [50] suggested that modeling employability in developing countries requires the use of economics and socio-political factors, as they are the reliable factors that influence concrete employability in such countries.…”

Career Recommendation System

“…PCRS can provide assistance to high school students who want to pursue engineering degrees. [1] Intelligent e-course recommender based on learning preferences: A clever e-course recommender tool has been developed, and it evaluates the e-courses in terms of how well they support different types of student learning styles, suggests learning objects that should be included to the courses, and it illustrates the suggestions and the rise in the help offered to those students by the course.…”

Exploratory Factor Analysis of Congolese Information Technology Graduates’ Employability: Towards Sustainable Employment

“…By using the retained factors as feature to predict IT employability in such countries, the above-mentioned engineers would build efficient IS/IT solution to mitigate youth unemployment problems and then attempt to achieve SDG target 8.6. Qamhieh et al (2020) asserted that the best way to build a relevant predictive and/or prescriptive model that predict relevantly the employability of students and attempts to achieve sustainable employability in developing countries is the inclusion of socio-political and economics as contextual features. Socially speaking, using the three retained factors as features to prescribe employability in research will help youths from unstable and unsecure zones to be aware of their socio-political backgrounds when enrolling to any IT course.…”