To further solve the problems of storage bottlenecks and excessive calculation time when calculating estimators under two different formats of massive longitudinal data, an examination data analysis and evaluation method based on an improved linear mixed-effects model is proposed in this paper. First, a three-step estimation method is proposed to improve the parameters of the linear-effects model, avoiding the complicated iterative steps of maximum likelihood estimation. Second, we perform spectral clustering based on test data on the basis of defining data attributes and basic evaluation rules. Finally, based on cloud technology, a cross-regional, multiuser educational examination big data analysis and evaluation service platform is developed for evaluating the proposed method. Experimental results have shown that the proposed model can not only effectively improve the efficiency of test data acquisition and storage but also reduce the computational burden and the memory usage, solve the problem of insufficient memory, and increase the calculation speed.

中文翻译：

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使用线性混合效应模型对教育考试研究进行新的海量大数据分析

为进一步解决在两种不同格式的海量纵向数据下计算估计量时存在的存储瓶颈和计算时间过长的问题，提出一种基于改进线性混合效应模型的检查数据分析与评价方法。首先，提出了一种三步估计方法来改进线性效应模型的参数，避免最大似然估计的复杂迭代步骤。其次，我们在定义数据属性和基本评估规则的基础上，根据测试数据进行谱聚类。最后，基于云技术，开发了跨区域、多用户的教育考试大数据分析与评价服务平台，对所提出的方法进行评价。