A Comparative Study of Machine Learning Approaches for Recommending University Faculty

Academic program selection is a vital decision as it plays a substantial role in enhancing opportunities in career paths. Numerous researches utilizing psychometrics have been conducted in recent years, and result reveals an evident association between academic fields and personality traits of students. To assist students in making an informed academic decision, this study analyzes the differences amongst the students of nine study programs in Bangladesh and envisages the appropriate academic choice among these nine fields for the prospective students by analyzing their academic grades, personality traits, and intelligence. For the experiment, we have collected data from 103 participants and used students' academic grades, Big-5 personality traits, and Factor B (Reasoning) of 16PF to develop classification models by utilizing two classification approaches. Results show that the Hierarchical classification approach using Random Forest Classifier outperformed the One-level Random Forest classifier approach by obtaining 96.1% accuracy for the 1st-level and 92.86%, 89.29%, and 94.74% respectively for 2nd-level. The findings of this study demonstrate that the proposed framework has great potential in assisting prospective students to make an informed decision to determine the suitable higher study options while unlocking human potential.