Nanda Alfan Kurniawan, Nur Hidayah, S'Dun Akbar, M. Ramli, Husni Hanafi, Fifi Khoirul Fitriyah, Destanika Dhiffa Ralianti, Mariani Binti Md Nor, Baraka Manjale Ngussa
Today's students born as Digital Natives face an increasingly complex job market and often struggle to make appropriate and confident career decisions. These difficulties are not always recognized, as many students delay their choices due to internalized expectations, perfectionism, or social dependency. This study aims to uncover latent career decisionmaking profiles among vocational high school students by developing a multidimensional digital assessment and applying unsupervised machine learning techniques to identify behavioral patterns. Data were collected through a web-based survey of Multidimensional Career Decision Making Scale (MCDMS). Reliability testing confirmed the internal consistency of the scale. Using K-means clustering, the study identified three meaningful clustered model of Delayed Idealists, Dependent Deciders , and Withdrawn Deciders. These cluster reflect not only conscious decision-making tendencies but also subconscious multidimensional patterns that may be overlooked by conventional assessments. This study demonstrates how data mining, especially machine learning, can enhance digital career assessments by depth exploration of the multidimensional patterns. This study contributes as educational data science practice, especially the applied of machine learning modeling for psychometric measurement. Future research might enhance the data validation through longitudinal studies, and might develop adaptive digital career guidance and counseling platforms. © 2025 IEEE.
Universitas Mulawarman, Dept. of Guidance and Counseling, Samarinda, Indonesia; Universitas Negeri Malang, Dept. of Guidance and Counseling, Malang, Indonesia; Universitas Negeri Malang, Dept. of Primary Education, Malang, Indonesia; Universitas Nahdlatul Ulama Surabaya, Dept. of Primary Education, Surabaya, Indonesia; Segi University, Dept. of Childhood Education, Petaling Jaya, Malaysia; Bugema University, Dept. of Curriculum and Instruction, Kiwenda, Uganda
