GOAL Master Dataset 2024-2025

The GOAL Master Dataset (2024-2025) is a high-fidelity synthetic large-scale dataset designed to validate the technical and mathematical claims of the G.O.A.L. framework. It simulates a dynamic higher-education environment involving 250,000 learners over a two-year academic cycle.

Core Data Characteristics
Volume: 250,000+ rows and 13+ technical columns.
Temporal Scope: Continuous timestamps ranging from January 2024 to December 2025, allowing for longitudinal analysis of student growth.

The dataset is specifically structured to perform Ablation Studies (comparing traditional vs. GOAL-based grading) and Sensitivity Analysis (testing the stability of the Global Aggregator Formula against exam outliers). It provides the empirical evidence required to prove that the "Andragogical Clock" and "Multi-Track Routing" result in higher cognitive growth compared to uniform pedagogical models.

GOAL主数据集（2024-2025）是一款高保真大规模合成数据集，旨在验证G.O.A.L.框架的技术与数学论断。该数据集模拟了动态高等教育环境，覆盖25万名学习者，周期为两个完整学年。

核心数据特征
数据规模：超25万条记录，包含13个以上技术维度字段。
时间范围：采用2024年1月至2025年12月的连续时间戳，支持学习者成长的纵向分析。

本数据集专为开展消融实验（对比传统评分与基于GOAL的评分方式）与敏感性分析（测试全局聚合公式对抗考试异常值的稳定性）而构建。其可为证明“成人学习时钟”与“多轨分流”相较于统一教学模式能带来更优认知成长提供必要的实证依据。