Driven by global educational reforms, interdisciplinary learning has become a key approach to cultivating well-rounded, innovative talent. However, effectively assessing students' adaptability in interdisciplinary...
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Driven by global educational reforms, interdisciplinary learning has become a key approach to cultivating well-rounded, innovative talent. However, effectively assessing students' adaptability in interdisciplinary learning environments remains a significant challenge in educational research. With the rapid development of image recognition technology, behavior-based intelligent analysis offers new opportunities for adaptability assessment by capturing students' behavioral performance in real-time and dynamically. Traditional approaches, such as surveys and interviews, are limited by subjectivity and inefficiency, making them insufficient for the precise, real-time analysis required in interdisciplinary settings. This study defines the key behavioral indicators of adaptability in interdisciplinary learning environments, develops an algorithm for detecting student behavior, and evaluates adaptability based on the detected results. An intelligent evaluation system is constructed to provide educators with objective data support, thereby enhancing teaching effectiveness and optimizing interdisciplinary learning environments.
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