The Role of Adaptive Learning in the Training of Electronics and Automation Engineers
DOI:
https://doi.org/10.57125/FED.2022.25.03.8Keywords:
adaptive learning, engineering education, AI-driven learning, problem-solving skills, student engagement, Industry 4.0, automation trainingAbstract
The rapid advancement of Industry 4.0 and 5.0 necessitates a transformation in engineering education, particularly in electronics and automation. Conventional approaches to instruction do not meet the different needs of learners, which contributes to skill deficiencies within the workforce. This research analyzes the impact of adaptive learning on academic achievement, problem-solving abilities, and engagement relative to other traditional methods. A quasi-experimental design was utilized for 124 engineering students separated into two groups: an experimental group receiving AI-based adaptive learning and a control group taught with lectures followed by manual drills. Quantitative information was retrieved through pre and post-tests, while qualitative data was acquired through surveys and teacher observations. The findings showed that the adaptive learning portion significantly outperformed the other group in later CG scores (33.3% vs 12.0%, p < 0.001, Cohen’s d = 1.26) and student participation (84.7% vs 62.3%). Main engagement drivers were identified as personalized learning paths, immediate feedback, and slack in the learning pacing. Other noted limitations included the lack of technological infrastructure and difficulties in initial adaptation. These results offer evidence that adaptive learning can narrow the gap between engineering education and industry requirements and recommend incorporating its use into higher education programs as a blended learning and stronger AI-cantered projects. Further studies ought to look into the effects of adaptive learning on workforce preparedness over time, personalized learning strategies, and large-scale implementation across all engineering fields.
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