工程专业学生低维倦怠风险分类：需求恢复互动特征的作用

Student burnout is a rapidly growing concern, with prior research linking its development to a wide range of psychological, behavioral, and contextual factors. Machine learning approaches have recently shown strong predictive performance for identifying burnout, but typically rely on high-dimensional datasets impractical for real-world use. Whether burnout can be reliably identified from a minimal set of variables remains underexplored. This study addresses that gap using a low-dimensional predictive framework in undergraduate engineering students. Grounded in Conservation of Resources theory,