Revealing Learning Potential: Construction and Application of an Adaptive Hierarchical Diagnostic Model for Students’ Zone of Proximal Development

Diagnosing the zone of proximal development (ZPD) is an important approach to reveal students’ learning potential and achieve personalized learning. However, traditional teaching predominantly relies on static assessments and teachers’ experience to vaguely grasp students’ ZPD, lacking scientific analysis and quantitative representation. In order to accurately diagnose students’ ZPD, this paper constructed an adaptive hierarchical diagnostic model for students’ ZPD, and elaborated on four modules: hierarchical representation of students’ ZPD, design of an evaluation index system for students’ ZPD based on disciplinary competencies, design of the calculation process for adaptive hierarchical diagnosis of ZPD and intelligent visualization of ZPD based on system development. Subsequently, taking the information technology subject as an example, through conducting a teaching application experiment of the adaptive hierarchical diagnostic model for students’ ZPD, it was found that this model can promote the construction of students’ ZPD, improve academic performance, and enhance subject ability. Thus, the effectiveness and practicality of this model were verified. The research in this paper provided new theoretical basis and practical experience for the intelligent diagnosis of students’ ZPD, and also offered beneficial references for personalized teaching based on ZPD in the digital era.