Exploring Engineering Specialisation Selection through Personality Traits, Interests and Hobbies

CONTEXT: Many institutions offer a common first year that exposes students to various engineering specialisations (e.g. civil, electrical, mechanical), to help students make informed decisions on choosing their specialisation upon entering their second year of study. However, constraints on specialisation coverage and student misconceptions can lead to mismatched choices, discontinuation of the degree, or uncertainty about long-term career directions.

GOAL: This study explores how first-year students' specialisation preferences align with their eventual enrolment. It also investigates whether personality traits, hobbies and interests can be used to construct specialisation profiles, with the broader goal of informing personalised guidance tools that support informed and confident decision-making.

APPROACH: This study utilises survey data collected from second-year and higher engineering students at Monash University in 2025. The data includes their chosen engineering specialisation, personality traits as per ITP Metrics, their self-indicated hobbies and discipline-specific interests. A hybrid fuzzy logic and logistic regression model was applied to identify potential specialisation profiles based on these attributes.

OUTCOMES: Prior results indicate that Monash University first-year students typically express interest in two or more specialisations (2.8, on average). This highlights a lack of clarity and decisiveness regarding their studies. In their ultimate enrolment, a significant 28% of students enrol outside of the specialisations initially indicated. Preliminary analyses from this study indicated that while personality traits alone provided insufficient variability to build reliable specialisation profiles, the integration of skills and hobbies enabled stronger clustering and enhanced the predictive accuracy of the model to 70% (for the top three specialisations). Nevertheless, model performance remains constrained by sample size and the limited granularity of the data.

CONCLUSIONS: This study demonstrates the potential for combining personality, engineering-related interests and hobby data to inform personalised specialisation guidance. These findings support the development of scalable, data-driven tools to help engineering students make more confident and informed specialisation decisions.