Abstract This decade, e-learning systems provide more interactivity to instructors and students than traditional systems and make possible a completely online (CO) education. However, instructors could not warn if a CO student is engaged or not in the course, and they could not predict his or her academic performance in courses. This work provides a collection of models (exploratory factor analysis, multiple linear regressions, cluster analysis, and correlation) to early predict the academic performance of students. These models are constructed using Moodle interaction data, characteristics, and grades of 802 undergraduate students from a CO university. The models result indicated that the major contribution to the prediction of the academic student performance is made by four factors: Access, Questionnaire, Task, and Age. Access factor is composed by variables related to accesses of students in Moodle, including visits to forums and glossaries. Questionnaire factor summarizes variables related to visits and attempts in questionnaires. Task factor is composed of variables related to consulted and submitted tasks. The Age factor contains the student age. Also, it is remarkable that Age was identified as a negative predictor of the performance of students, indicating that the student performance is inversely proportional to age. In addition, cluster analysis found five groups and sustained that number of interactions with Moodle are closely related to performance of students.

中文翻译：

---

完全在线学习中本科学生学业成绩的早期预测：一项为期五年的研究

摘要 这十年来，电子学习系统为教师和学生提供了比传统系统更多的交互性，并使完全在线 (CO) 教育成为可能。但是，教师无法警告 CO 学生是否参与课程，也无法预测他或她在课程中的学习成绩。这项工作提供了一系列模型（探索性因素分析、多元线性回归、聚类分析和相关性）来早期预测学生的学业成绩。这些模型是使用来自 CO 大学的 802 名本科生的 Moodle 交互数据、特征和成绩构建的。模型结果表明，对学业成绩预测的主要贡献由四个因素构成：访问、问卷、任务和年龄。访问因子由与学生访问 Moodle 相关的变量组成，包括访问论坛和词汇表。问卷因子总结了问卷中与访问和尝试相关的变量。任务因子由与咨询和提交的任务相关的变量组成。年龄因子包含学生年龄。此外，值得注意的是，年龄被确定为学生成绩的负面预测因素，表明学生成绩与年龄成反比。此外，聚类分析发现五个小组并坚持认为与 Moodle 的互动次数与学生的表现密切相关。任务因子由与咨询和提交的任务相关的变量组成。年龄因子包含学生年龄。此外，值得注意的是，年龄被确定为学生成绩的负面预测因素，表明学生成绩与年龄成反比。此外，聚类分析发现五个小组并坚持认为与 Moodle 的互动次数与学生的表现密切相关。任务因子由与咨询和提交的任务相关的变量组成。年龄因子包含学生年龄。此外，值得注意的是，年龄被确定为学生成绩的负面预测因素，表明学生成绩与年龄成反比。此外，聚类分析发现五个小组并坚持认为与 Moodle 的互动次数与学生的表现密切相关。