OBE视角下管理类课程学生数字画像构建及精准教学研究——以《组织行为学》课程为例

Student Digital Portrait and Precision Teaching in Management Courses under OBE—A Case Study of Organizational Behavior

摘要: 伴随慕课平台的广泛应用，高等教育正加快迈向数智化时代，亟需探索数据驱动的复合型管理人才培养新模式。然而，OBE改革在课程实施层面常因缺乏对学生学习过程的细粒度诊断而难以实现精准施策。文章以《组织行为学》课程学生多源数据为例，基于“理论修养、数据洞察、跨界视野、伦理治理”四维能力框架，构建学习行为指标体系，并采用K-means聚类识别学生画像。研究发现，学生可根据在不同模块学习内容上的相对投入倾向划分为六类具有显著差异的学习行为偏好群体，且不同画像群体在学习成效上存在显著差异。在此基础上，文章提出了优化模块内容供给、完善多元考核体系、嵌入学习策略微课程、强化视频交互设计以及建立动态预警机制等精准教学启示。

Abstract: With the widespread adoption of MOOC platforms, higher education is accelerating toward the era of digital intelligence, making it imperative to explore new data-driven models for cultivating interdisciplinary management talent. However, at the level of course implementation, OBE reform often struggles to achieve precise intervention due to the lack of fine-grained diagnosis of students’ learning processes. Using multi-source data from students enrolled in the Organizational Behavior course, this study constructs a learning behavior indicator system based on a four-dimensional competence framework consisting of theoretical cultivation, data insight, cross-disciplinary vision, and ethical governance, and employs K-means clustering to identify student portraits. The findings show that students can be classified into six groups with significantly different learning behavior preferences, and that these portrait groups also differ significantly in learning outcomes. On this basis, the study proposes several implications for precision teaching, including optimizing the provision of module content, improving a diversified assessment system, embedding micro-courses on learning strategies, strengthening video interaction design, and establishing a dynamic early-warning mechanism.