Both educational data mining and learning analytics aim to understand learners and optimise learning processes of educational settings like Moodle, a learning management system (LMS). Analytics in an LMS covers many different aspects: finding students at risk of abandoning a course or identifying students with difficulties before the assessments. Thus, there are multiple prediction models that can be explored. The prediction models can target at the course also. For instance, will this activity assessment engage learners? To ease the evaluation and usage of prediction models in Moodle, we abstract out the most relevant elements of prediction models and develop an analytics framework for Moodle. Apart from the software framework, we also present a case study model which uses variables based on assessments to predict students at risk of dropping out of a massive open online course that has been offered eight times from 2013 to 2018, including a total of 46,895 students. A neural network is trained with data from past courses and the framework generates insights about students at risk in ongoing courses. Predictions are then generated after the first, the second, and the third quarters of the course. The average accuracy that we achieve is 88.81% with a 0.9337 F1 score and a 73.12% of the area under the ROC curve.

中文翻译：

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有监督的学习框架：使用评估来识别有可能退出MOOC的学生

教育数据挖掘和学习分析都旨在了解学习者并优化教育环境（如Moodle，一种学习管理系统（LMS））的学习过程。LMS中的分析涵盖许多不同方面：在评估前寻找有可能放弃课程的学生或确定有困难的学生。因此，可以探索多种预测模型。预测模型也可以针对课程。例如，这项活动评估会吸引学习者吗？为了简化Moodle中预测模型的评估和使用，我们提取了预测模型中最相关的元素，并为Moodle开发了一个分析框架。除了软件框架，我们还提供了一个案例研究模型，该模型使用基于评估的变量来预测有可能退出大规模开放式在线课程的学生，该课程已从2013年至2018年提供了八次，其中包括46,895名学生。使用过去课程中的数据对神经网络进行训练，该框架会生成有关正在进行中课程中处于危险中的学生的见解。然后在课程的第一，第二和第三季度之后生成预测。我们获得的平均准确率为88.81％，F1得分为0.9337，ROC曲线下面积为73.12％。然后在课程的第一，第二和第三季度之后生成预测。我们获得的平均准确率为88.81％，F1得分为0.9337，ROC曲线下面积为73.12％。然后在课程的第一，第二和第三季度之后生成预测。我们获得的平均准确率为88.81％，F1得分为0.9337，ROC曲线下面积为73.12％。