Abstract
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.
Highlights
Nowadays, the era of big data affects many industries
One-sided, and complex characteristics of the educational examination quality evaluation process, as well as the resulting unscientific and nonobjective examination evaluation problems, based on the improved linear mixed effect model, we have developed a large amount of educational examination big data and the application analysis of the proposed method, including the following
To study the relationship between the calculation time and the number of subdataset blocks and illustrate the necessity of using the divide-and-conquer algorithm for block calculation, the data under the two sample sizes are divided into different dataset block numbers, and the estimated parameters are recorded. e relationship between the calculation time of sample (1) and sample (2) and the number of dataset blocks is shown in Figure 2. e data generated in the figure is the time used by the sample summation method
Summary
The era of big data affects many industries. Among them, the education industry has improved its educational examinations through the use of big data technology [1]. Educational examination big data analysis and evaluation is to mine the original test data of students, feedback a large amount of information and laws hidden in the data relationship, and assist the education department, teachers, and students to analyze the reasons for the formation of test results. These methods require repeated iterative calculations and optimization steps, which will lead to high calculation time and quantity cost when the amount of data is very large To solve such problems, [8] proposed a simple method for estimating variance components when studying random effect variable coefficient models. One-sided, and complex characteristics of the educational examination quality evaluation process, as well as the resulting unscientific and nonobjective examination evaluation problems, based on the improved linear mixed effect model, we have developed a large amount of educational examination big data and the application analysis of the proposed method, including the following. Application results have proved that the platform can effectively improve the efficiency of test data acquisition and storage and, at the same time, provide effective analysis tools for education supervisors and implementation departments
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