发表机构
Nakamura Gakuen University; Hiroshima University（中村学园大学; 广岛大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对合成教育数据保真度评估的不足，提出基于学习者每周邻近图连通分量的结构性检查，发现合成数据跨学期变化幅度显著缩小且峰值周错位，并指出常规阈值规则导致比较无效。
AI 中文摘要
教育记录的二次使用日益通过平台进行，这些平台共享数据集的差分隐私合成版本，并应请求针对真实数据验证特定发现。合成版本通过比较每个变量的汇总统计量来评估，然而报告的确认率表明，此类比较并不能预测哪些发现能够存续。我们提出一项结构性检查：跨学期跟踪的学习者每周邻近图的连通分量数量。在四个年度队列的初中学习习惯日志中，合成版本再现了该数量的水平及每周划分的形状，但在常见工作点下，其跨学期的变化幅度比真实数据小2.6至4.9倍，无一例外，且这些变化发生在不同的周：合成队列突出学期考试周，而真实队列则不然。我们还表明，设置图阈值的常规规则会使两个数据集之间的朴素比较无效，并以我们自身的一个错误为例说明。在所有四个队列中，真实曲线也可与其保留边际的替代版本区分开来，而四个合成版本中有三个无法区分，这一比较无需真实数据；这些差异可追溯至生成器所获得的数据。
英文摘要
Secondary use of educational records is increasingly mediated by platforms that share a differentially private synthetic version of a dataset and validate specific findings against the real data on request. The synthetic version is evaluated by comparing summary statistics of each variable, yet reported confirmation rates suggest that such comparisons do not predict which findings survive. We propose a structural check: the number of connected components of a weekly proximity graph over learners, tracked across a term. Across four annual cohorts of lower-secondary study-habit logs, the synthetic versions reproduced the level of this quantity and the shape of the weekly partition, but its variation across the term was between 2.6 and 4.9 times smaller than in the real data at a common working point, without exception, and those changes fell in different weeks: the synthetic cohorts single out the term's examination weeks and the real cohorts do not. We also show that a routine rule for setting the graph threshold makes naive comparisons between two datasets invalid, and illustrate this with an error of our own. The real curves are also distinguishable from marginal-preserving surrogates of themselves in all four cohorts, where three of the four synthetic ones are not, a comparison that needs no real data; these differences trace to what the generator was given.
CommentsAccepted for presentation at 1st Workshop of Trustworthy Educational Data Sharing and Secondary Use: ReLEAF Data Challenge, The 34th International Conference on Computers in Education
