Despite the increasing popularity of massive open online courses (MOOCs), many suffer from high dropout and low success rates. Early prediction of student success for targeted intervention is therefore essential to ensure no student is left behind in a course. There exists a large body of research in success prediction for MOOCs, focusing mainly on training models from scratch for individual courses. This setting is impractical in early success prediction as the performance of a student is only known at the end of the course. In this paper, we aim to create early success prediction models that can be transferred between MOOCs from different domains and topics. To do so, we present three novel strategies for transfer: 1) pre-training a model on a large set of diverse courses, 2) leveraging the pre-trained model by including meta information about courses, and 3) fine-tuning the model on previous course iterations. Our experiments on 26 MOOCs with over 145,000 combined enrollments and millions of interactions show that models combining interaction data and course information have comparable or better performance than models which have access to previous iterations of the course. With these models, we aim to effectively enable educators to warm-start their predictions for new and ongoing courses.
翻译:尽管大规模开放式在线课程越来越受欢迎,但许多学生仍受到高辍学率和低成功率的影响。因此,及早预测学生成功参加有针对性的干预对于确保没有学生在课程中落后至关重要。在成功预测MOOC方面有大量的研究,主要侧重于从零开始的个别课程的培训模式。这种背景在早期成功预测方面是不切实际的,因为学生的成绩仅在课程结束时才为人所知。在本文件中,我们的目标是建立早期成功预测模型,这些模型可在不同领域和专题的MOOC之间转让。为了做到这一点,我们提出了三种新的转让战略:(1) 在一个大型不同课程中先行培训一个模型,(2) 利用预先训练过的模型,将课程的元信息纳入课程,(3) 对以往课程的迭代模式进行微调。我们在26个MOOC上进行的实验显示,将互动数据与课程信息相结合的模型比以往的版本都具有可比较或更好的性能。我们通过这些模型,有效地使教育工作者能够开始对新的和正在进行的课程进行暖化预测。