发现学生越依赖数字设备，网络一坏越不高兴，需针对性保障关键用户。
The Dependency Divide: An Interpretable Machine Learning Framework for Profiling Student Digital Satisfaction in the Bangladesh Context
- 按学习行为分三类学生，用机器学习与可解释分析找满意度关键因素。
- 发现高投入学生在网速不稳时满意度反而下降，形成‘依赖陷阱’。
- 建议优先保障高依赖用户网络可靠性，比平均投入效果好两倍以上。
背景：尽管资源匮乏地区数字接入快速扩展，但学生对数字学习平台的满意度差异显著，传统数字鸿沟框架无法解释此现象。目的：本文提出‘依赖鸿沟’新框架，指出高度参与的学生在基础设施故障下可能面临条件性脆弱，挑战了‘参与即受益’的普遍假设。方法：对孟加拉国396名大学生开展横断面研究，采用三阶段分析：（1）基于稳定性验证的K-原型聚类识别学生画像；（2）针对各画像使用随机森林结合SHAP与ALE分析确定满意度驱动因素；（3）通过倾向得分匹配进行交互效应检验。结果：识别出三类人群：随意参与者（58%）、高效学习者（35%）、超投入者（7%）。教育设备使用时间与网络可靠性间存在显著交互作用（η = 0.033, p = 0.028），表明只有在稳定网络下，高参与才提升满意度。超投入学生因复杂数字流程最易受干扰。政策模拟显示，针对高依赖用户的可靠性优化，回报是均等干预的2.06倍。结论：在基础设施脆弱环境中，能力可能变成负担。数字转型政策应优先保障依赖型用户、建立应急机制，并开展依赖风险教育，而非一味鼓励参与。
原文摘要 · Abstract (English)
Background: While digital access has expanded rapidly in resource-constrained contexts, satisfaction with digital learning platforms varies significantly among students with seemingly equal connectivity. Traditional digital divide frameworks fail to explain these variations. Purpose: This study introduces the "Dependency Divide", a novel framework proposing that highly engaged students become conditionally vulnerable to infrastructure failures, challenging assumptions that engagement uniformly benefits learners in post-access environments. Methods: We conducted a cross-sectional study of 396 university students in Bangladesh using a three-stage analytical approach: (1) stability-validated K-prototypes clustering to identify student profiles, (2) profile-specific Random Forest models with SHAP and ALE analysis to determine satisfaction drivers, and (3) formal interaction analysis with propensity score matching to test the Dependency Divide hypothesis. Results: Three distinct profiles emerged: Casually Engaged (58%), Efficient Learners (35%), and Hyper-Engaged (7%). A significant interaction between educational device time and internet reliability (\b{eta} = 0.033, p = 0.028) confirmed the Dependency Divide: engagement increased satisfaction only when infrastructure remained reliable. Hyper-Engaged students showed greatest vulnerability despite or because of their sophisticated digital workflows. Policy simulations demonstrated that targeted reliability improvements for high-dependency users yielded 2.06 times greater returns than uniform interventions. Conclusions: In fragile infrastructure contexts, capability can become liability. Digital transformation policies must prioritize reliability for dependency-prone users, establish contingency systems, and educate students about dependency risks rather than uniformly promoting engagement.
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