Multi-Criteria Evaluation Of Student Career Readiness Using An Intelligent Topsis-Based Decision Support System
TL;DR
It is demonstrated that TOPSIS can effectively rank and classify student career readiness based on multiple assessment criteria and can support higher education institutions in identifying students’ readiness levels and developing more objective, targeted, and data-driven career development strategies.
Abstract
The assessment of student career readiness requires a systematic approach that considers multiple competencies and experiences relevant to workplace demands. This study develops an intelligent decision support system based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to evaluate and map student career readiness. The study uses 20 student samples and five assessment criteria: field-specific competence (C1), internship experience (C2), communication skills (C3), digital literacy (C4), and English language proficiency (C5). Each criterion is assigned a weight based on its relative importance. The TOPSIS method involves decision matrix construction, normalization, weighted normalization, determination of positive and negative ideal solutions, distance calculation, and preference value calculation. The results show that Y11 achieved the highest preference value of 0.8296, followed by Y6 (0.7951), Y19 (0.7274), Y5 (0.7116), and Y2 (0.6999), while Y9 obtained the lowest preference value of 0.3112. Based on the classification results, 1 student (5%) was categorized as Highly Ready, 5 students (25%) as Ready, 12 students (60%) as Moderately Ready, and 2 students (10%) as Not Ready. These findings demonstrate that TOPSIS can effectively rank and classify student career readiness based on multiple assessment criteria. The proposed system can support higher education institutions in identifying students’ readiness levels and developing more objective, targeted, and data-driven career development strategies.