This study develops and validates a human-centered adaptive learning system that integrates Human Factors Engineering (HFE) and Artificial Intelligence (AI) to support programming education. The system adjusts learning strategies based on learners' behavioral indicators and self-reported psychological states, including motivation, interest, and confidence. Grounded in adaptive learning theory, the study proposes and tests hypotheses regarding the associations and interaction effects of these factors on learning effectiveness. A single-group pre/post empirical design with 100 participants was employed, incorporating exploratory factor analysis, regression modeling, and user satisfaction surveys. Results indicate that both motivation and interest are significantly associated with improved learning outcomes, and their interaction demonstrates a synergistic effect. The system's modular architecture—comprising behavioral data collection, learner modeling, strategy generation, and feedback—was positively evaluated for usability and engagement. While the findings confirm theoretical associations within the adaptive environment, causal claims and comparative effectiveness against non-adaptive systems require future controlled studies. By combining ergonomic interface design with AI-driven adaptivity, this research contributes to educational ergonomics and adaptive learning literature, offering a replicable framework and practical insights for designing intelligent, user-aligned instructional systems.
扫码关注我们
求助内容:
应助结果提醒方式:
