利用LMS Moodle数字足迹的通用特性预测课程的学习成绩

R. Esin, T. A. Kustitskaya, M. Noskov
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引用次数: 1

摘要

学生保留预测是学习分析中最重要的问题之一。在全球范围内对高等教育这一主题的研究相当广泛,在高校中也有成功实施教育支持服务的案例。文献分析表明,俄罗斯科学界和教育界对这一问题的兴趣日益浓厚。与此同时,俄罗斯教育的特殊性不允许将外国经验直接转移到国内教育体系中。研究表明,课程中教育课程的学习成绩预测模型对预测学生保留率有重要贡献。作者提出了一个预测学习成绩系统的结构模型,其中包括一个基于数字足迹广义指标的通用模型,一个考虑到特定学科学习细节的基于课程的模型,以及一个基于学生教育概况的模型。在实证研究中,我们基于LMS Moodle学生数字足迹的通用指标,训练了5个中期评估成绩的早期预测模型。最准确的模型,尤其是在前半学期,是逻辑回归、随机森林和梯度增强的集合平均模型。研究发现,通用模型能够有效地检测出该学科的风险学生,确定了进一步改进通用绩效预测模型的方向,并制定了将所提出的方法扩展到其他教育机构以创建学生保留预测系统的条件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting academic performance in a course by universal features of LMS Moodle digital footprint
Student retention prediction is one of the most important problems of learning analytics. In the global scope research on the topic for higher education is rather extensive, there are cases of successful implementation of education support services in universities. The literature analysis shows of the growing interest in this problem in the Russian scientific and pedagogical community. At the same time, the specifics of Russian education does not allow direct transfer of foreign experience into the domestic educational system.The study reveals that a significant contribution to predicting student retention can be made by models for predicting academic performance in educational courses of the curriculum. The authors propose a structural model of a system for predicting academic performance, which includes a universal model based on generalized indicators of the digital footprint, a course-based model that takes into account the specifics of learning in a particular discipline, and a model based on the student’s educational profile.In the empirical study we trained 5 models for early prediction of interim assessment grades based on the universal indicators of the LMS Moodle student digital footprint. The most accurate model, especially in the first half of the semester, turned out to be ensemble-averaging models of logistic regression, random forest and gradient boosting. It was found that universal models are effective for detection of at-risk students in the discipline, the directions for further improvement of the universal model of performance prediction were determined and conditions for scaling the proposed approach to create a prognostic system for student retention to other educational institutions were formulated.
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