Apriori algorithm applied in job forecasting with elective tracking

Discovering patterns for predicting student enrolment in a college program, student performance, and other aspects of higher education is one strategy to improve quality. On the other hand, when it comes to enrolment, it is critical to select an acceptable elective for students. One of the most well-known data mining algorithms is the Apriori Algorithm. It is used to locate frequently occurring item sets and related association rules. The research study aims to provide an elective selection to students to achieve a career area focus by using the Apriori Algorithm that uses the Association Rule Mining in recommending an elective subject. There are two sets of data collected – the first was the training data from 2018 graduates, and the second was the testing data from incoming third year students. The training data was modeled using a simulation in WEKA to find out what associated subjects are frequent in the students’ grades. The result from this model found is the best rules used in the elective configuration. The research shows that identified challenges on the current process of elective evaluation were ranked according to (1) Not enough understanding of the elective coursework that will provide support for occupational choices; (2) Cannot articulate what skills are expected to gain from the elective, and (3) Less knowledge in the elective related to career options. The research concludes that the evaluation of the respondents can help guide program coordinators and students to have an area of focus on their career path based on grade evaluation and elective of choice.