Personalized Career-Path Recommendation Model for Information Technology Students in Indonesia

Abstract

One of the challenging decisions for students is taking a job specialization. To make their decisions, they use subjective perceptions of friends or family due to the lack of guidance and limited resources. This increases the risk of dissatisfaction with the work environments. To address these drawbacks, this study presents a personalized career-path recommendation model (CPRM) to provide guidance and help college students choose information technology jobs. The design of the CPRM is based on the personalized Naïve Bayes (p-NB) algorithm with three primary sources: job profiles, personality types, and subjects. The association between personality type and college students was established using samples of 104 computer science students enrolled in private universities in Indonesia. CPRM was implemented as a web-based application. This study evaluated the model by measuring the quality of the recommended items to determine whether the proposed model is well accepted by users. The model considers educational data mining grounded theory (EDM-GT) data integration and hierarchically related concepts. CPRM has been validated by Information Technology (IT) professionals and three psychologists in Indonesia through focus group discussions. The evaluation results showed that more than 83% of respondents were satisfied with the recommendation model. Hence, CPRM can provide automatic academic advisors and guidance to computer science students interested in pursuing careers in IT jobs. The result shows that CPRM is the first career path recommendation model based on EDM-GT to target the computer science community in Indonesia.

Keywords

• Educational data mining
• grounded theory
• information technology
• naïve bayes
• career path recommendation model