Immediate and meaningful feedback is a key element in improving academic performance in computational foundational disciplines. This study aimed to evaluate the impact of gamified strategies in the feedback process of the Programming Fundamentals course. The intervention was conducted with a population of 31 first-level students from the Mechatronics Engineering program at Universidad Técnica del Norte, of which 15 students voluntarily participated from the same course. Activities were designed using the Kahoot! platform, focused on reinforcing theoretical and practical content related to the course syllabus and the programming languages Java and Python. The adopted methodology was applied in nature, with a descriptive approach and exploratory scope. The results showed increased active participation, improved retention of key concepts, and a more positive attitude towards programming learning. In addition, recurring error patterns were identified, which served as input for tailored feedback. The use of playful dynamics fostered a collaborative learning environment, contributing to the development of cognitive and metacognitive skills. It was concluded that the integration of educational technologies with gamification elements enhances the effectiveness of feedback processes, increases students' intrinsic motivation, and enables timely identification of knowledge gaps. This approach is especially valuable in programming education, where active engagement and continuous practice are critical to academic success.

This study investigates the performance of a 1.5 kW, 20,000 r/min universal motor under AC and DC supply by combining experimental characterization, MATLAB/Simulink modeling, and two-dimensional magnetostatic analysis using Finite Element Method Magnetics (FEMM). A motor–generator test bench was used to record electrical variables and shaft speed at different load levels, from which electromagnetic torque was estimated. Simulink represented the average electromechanical response through a lumped-parameter model, whereas FEMM was used to examine flux distribution, magnetic saturation, and rotor-position dependence. The experiments and Simulink model consistently showed decreasing speed and increasing torque as load increased, whereas FEMM clarified the associated magnetic saturation and rotor-position dependence. Simulink reproduced the general operating trends but exhibited quantitative deviations associated with its linear magnetic representation. FEMM indicated the onset of saturation between 2 and 3 A and showed that the calculated torque depended strongly on angular position. The coordinated comparison clarifies the complementary scope of experimental, system-level, and field-based analyses while identifying the limitations imposed by indirect torque estimation and magnetostatic modeling.