互联网环境下学生成绩预测模型的随机建模方法

E. Khakata
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引用次数: 0

摘要

随机建模考虑在预测过程中使用随机变量。随机变量是从不同的场景中生成的,以便生成可能的输出。因此,生成的输出用于指示在未来某个日期可能发生或可能不发生的非常罕见的情况的可能性。由于学习部门拥有大量可用的教育数据,这些数据构成了预测学生在互联网工作环境中表现所需的输入数据的基础。本文提出了一种用于预测建模的随机微分方程(SDE)方法。这种方法分析了大学学生的数据,从而产生了学生的表现轨迹。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Stochastic Modeling Approach to Student Performance Prediction Modeling (SPPM) on an Internet-Mediated Environment
Stochastic modelling takes into consideration the use of random variables in the prediction process. The random variables are generated from different scenarios in order to generate a possible output. As a result, the generated output is used to indicate the likelihood of very rare occurrence scenarios which may or may not take place at a future date. With the vast availability of educational data that is available within the learning sector, this data forms the basis of input data that is required for the prediction of student performance within internet-worked environments. This paper proposes a Stochastic Differential Equation (SDE) approach to prediction modelling. This approach analyses data from students within universities leading to the generation of a student performance trajectory.
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