<![CDATA[Objective: This study aimed to address the prevalent difficulty among electrical engineering students in applying theoretical knowledge to the practical interpretation of transformer diagnostic data. The primary objective was to assess the effectiveness of a purpose-built diagnostic software application in improving the analytical skills required for this task. Method: The research employed a quasi-experimental, one-group pretest-posttest design involving a sample of 36 undergraduate students in a D4 Electrical Engineering program. Participants' analytical skills were measured before and after the intervention, which consisted of training with the diagnostic application. Data were collected through quantitative tests, student perception surveys, and instructor observation sheets. The analysis involved paired sample t-tests and N-Gain calculations, with findings triangulated using qualitative feedback to enhance validity. Result: The intervention yielded highly positive outcomes, demonstrating a statistically significant improvement in students' analytical abilities. The mean score rose from 56.19 to 82.58, and an average N-Gain score of 0.61 classified the application's effectiveness in the "medium improvement" category. These quantitative findings were strongly supported by qualitative data, wherein students reported the application to be highly intuitive and effective in transforming passive learning into an active, contextualized, and confidence-boosting experience. Novelty: The novelty of this research lies in providing empirical evidence for a scalable and effective pedagogical tool that bridges the critical gap between academic theory and industry-required practical skills. This study presents a validated software-based solution to a persistent challenge in vocational engineering education, demonstrating a tangible method for better preparing graduates to meet professional demands in transformer diagnostics.]]>
Copyrights © 2025
