Predicting the Job-Relatedness of Scholars Through Data Integration of Two Information Systems

It is universally accepted that human resource is the foundation of a country's economy. However, education is accessible to less privileged members of society. Hence, academic scholarship grants have been created to provide less fortunate yet deserving students with access to education and build a better future. This study aims to determine the appropriate attributes in developing the dataset for the study's predictive model. Two information systems served as the source of datasets: the Online Scholarship Application Information System (OSAIS) and the electronic-Graduate Tracer Survey (eGTS) Questionnaire. In addition, four school year graduates were used as raw data containing nineteen (19) academic programs. Finally, the standard Query Language (SQL) tool merged and filtered the extracted data from two information systems. The study uses two information systems currently employed at the university; this includes (1) The Online Scholarship Application Information System (OSAIS); and (2) the electronic-Graduate Tracer Survey (eGTS) Questionnaire for Higher Education as a study framework. Among the attributes, gender significantly prevails in predicting the job-relatedness of academic scholarship grantees.