BACKGROUND Early prediction of students’ learning performance using data mining techniques is an important topic these days. The purpose of this literature review is to provide an overview of the current state of research in that area. METHOD We conducted a literature review following a two-step procedure, looking for papers using the major search engines and selection based on certain criteria. RESULTS The document search process yielded 133 results, 82 of which were selected in order to answer some essential research questions in the area. The selected papers were grouped and described by the type of educational systems, the data mining techniques applied, the variables or features used, and how early accurate prediction was possible. CONCLUSIONS Most of the papers analyzed were about online learning systems and traditional face-to-face learning in secondary and tertiary education; the most commonly-used predictive algorithms were J48, Random Forest, SVM, and Naive Bayes (classification), and logistic and linear regression (regression). The most important factors in early prediction were related to student assessment and data obtained from student interaction with Learning Management Systems. Finally, how early it was possible to make predictions depended on the type of educational system.

中文翻译：

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通过数据挖掘对学生学习成绩的早期预测：系统回顾。

背景技术目前，使用数据挖掘技术对学生的学习表现进行早期预测是一个重要的话题。本文献综述的目的是概述该领域的研究现状。方法我们按照两步程序进行了文献综述，使用主要搜索引擎查找论文并根据某些标准进行选择。结果 文件搜索过程产生了 133 个结果，其中 82 个被选中以回答该领域的一些基本研究问题。选定的论文按照教育系统的类型、应用的数据挖掘技术、使用的变量或特征以及早期准确预测的可能性进行分组和描述。结论 分析的大多数论文是关于在线学习系统和中等和高等教育中的传统面对面学习；最常用的预测算法是 J48、随机森林、SVM 和朴素贝叶斯（分类），以及逻辑和线性回归（回归）。早期预测中最重要的因素与学生评估和从学生与学习管理系统互动中获得的数据有关。最后，多早做出预测取决于教育系统的类型。早期预测中最重要的因素与学生评估和从学生与学习管理系统互动中获得的数据有关。最后，多早做出预测取决于教育系统的类型。早期预测中最重要的因素与学生评估和从学生与学习管理系统互动中获得的数据有关。最后，多早做出预测取决于教育系统的类型。