Towards the Development of a Career Path Recommender System for Senior High School in Selected Public Schools using Multi-Label Classification

The Philippines challenged accepting reforms that shifted 10-year to the k-12 basic education program, adding two years to the students’ basic education. Each graduate student from the grade 10 level must choose a career path that will give them the skills they need to be future-ready. Given the long-term effects on a student’s career, selecting the SHS track or strand is one of the most important life decisions they will ever make. In this paper, a career path recommender system will introduce to avoid a mismatch of strands/tracks and help the students choose the possible career path in Senior High School (SHS) that fits their taste and capability. The researcher will collect related data from six (6) selected public schools offering grade 10 in Valenzuela City. And will explore different student data such as family background, big five personalities, grades, IQ test, and some tools in the Multiple Development Toolkit from Department of Education (Self-assessment and career interest cluster, Personal career exploration). The study will use content-based filtering and classification algorithm in machine learning like Decision trees (DT), K-Nearest Neighbor (KNN), Naïve Bayes (NB), and Support Vector Machine (SVM) that will serve as base-classifier in the multi-label classification.