Abstract
Selecting the appropriate academic track is a critical decision for students, as misalignment between program requirements and individual cognitive, personality, and competency profiles can significantly impact academic performance, persistence, and overall educational outcomes. Traditional educational recommender systems often rely solely on skill matching or on the correlation of interests, failing to account for the dimension of competency that is required for success in specific academic tracks. This paper introduces a novel Multi-Dimensional Psychometric Alignment (MDPA) algorithm that moves beyond simple rank-order correlation between skills and programs by jointly integrating multiple psychometric perspectives and evaluating both preference similarity and competency sufficiency. Based on a structured synthesis of Cognitive Preferences (MBTI), Cognitive Modalities (Gardner’s Multiple Intelligences), and Personality Stability (Big Five), the proposed profile captures complementary dimensions of student readiness that are usually examined separately in prior educational recommender systems. Then applies an alignment algorithm-which is based on a hybrid similarity metric that fuses Spearman’s Rank Correlation (Interest Shape) with Weighted Euclidean Distance (Competency Magnitude), enforced by non-linear threshold penalties for critical traits- in order to find the best options for students. This approach constitutes a deterministic, explainable recommender system whose novelty lies in combining heterogeneous psychometric evidence with an explicit magnitude–shape matching mechanism and threshold-based academic viability constraints. Our approach is validated through a case study of university students in Kazakhstan, and the results demonstrate how “academic fit” is better modeled as a function of both interest pattern and trait sufficiency, offering a robust alternative to “black-box” skill-based recommenders.