A Data-Driven Profile-based Analytics for Career Path and Upskilling Recommendations in HRS - Paul Nigel S. Abalos (International Journal of Intelligent Systems and Applications in Engineering, 2024)
This quantitative-experimental research aimed to develop a profile-based data analytics module for career path and upskilling recommendations module for the Data-Driven Human Resource Analytics System (DDHRAS) of the largest state university in Ilocos Region and address common analytics results heavily based on skills which do not fully include other useful data in some sectors, such as the academe in which the researchers belong to. Hence, the study is motivated to come up with an analytics module that is close to the university HRS by incorporating significant employee personal data, educational background, seminars and trainings, skills and competencies, board examinations, job performance score, work experience, and tenure. A scoring matrix based on human resource policies was devised and paired with Word2vec algorithm to evaluate which profile composition criteria each employee has an advantage of or needs to improve to qualify for promotions. Dataset covers 628 personnel data sheets of employees for School Years 2020 to 2023 from different campuses of the subject institution. The sequential neural network to predict career path and upskilling recommendations was utilized and resulting models were evaluated using model classification performance metrics. Experimental results show that the models developed have consistently obtained an average accuracy of 88.99% during performance validation, indicative of rigid classification performance. The implication of the developed models, when fully integrated and implemented within the existing system of the HRD, can provide an alternative faster career path and upskilling recommendations to lessen the burden of HRD evaluators, thus, save valuable resources.