面向在线教育场景的异构数据生成工具

Heterogeneous data generation tools for online education scenarios

在数字化教育应用领域,在线课堂等平台的开发人员在追求数据驱动的优化过程中,面临着隐私问题和现有数据集规模不足的挑战.针对此,构建了一种适应教育特性的异构数据模型,并实现了相应的数据生成工具(E-Tools),用于模拟复杂教育场景下的数据交互.实验表明,该工具在多种数据规模下,都能保持高效的数据生成速度(64~74 MB·s^(-1)),展现了良好的线性扩展能力,验证了所提模型的有效性及工具生成较大数据量的能力.同时,设计了反映学生学习行为的异构数据查询负载,为教育平台的性能评估与优化提供了强有力的支持.

In the digital education application domain,developers of platforms such as online classrooms face the challenges of privacy issues and existing datasets’insufficient size in their pursuit of data-driven optimization.To address this,a set of heterogeneous data models adapted to the characteristics of education were constructed,and corresponding data generation tools(E-Tools)that can be used to simulate data interactions in complex educational scenarios were implemented.Experimental results have shown that the tool can maintain an efficient data generation speed(64–74 MB·s^(-1))under a variety of data sizes,demonstrating good linear scaling ability,which validates the model’s effectiveness and the tool’s ability to generate larger data volumes.A heterogeneous data query load reflecting students’learning behaviors was also designed to provide strong support for performance evaluation and the education platform’s optimization.