论文标题
预测MOOC辍学仅使用第一周活动中的两个易于获得的功能
Predicting MOOCs Dropout Using Only Two Easily Obtainable Features from the First Week's Activities
论文作者
论文摘要
尽管大规模开放的在线课程（MOOC）平台以一种新的独特方式提供知识，但大量的辍学率是一个重要的缺点。几个功能被认为有助于学习者流失或缺乏兴趣，这可能导致脱离或辍学。陪审团仍在出现哪些因素是最合适的预测因素。但是，文献同意，早期预测对于允许及时干预至关重要。尽管功能丰富的预测因子可能有最佳准确性的机会，但它们可能很笨拙。这项研究旨在通过比较几种机器学习方法，包括随机森林，自适应提升，XGBoost和梯度启动分类器，从而预测学习者辍学者。结果表明，使用最少的2个功能显示了有希望的精度（82％-94％）。我们表明，即使后者部署了多个功能，即使在后者部署了几个功能的情况下，精度获得了跑赢大盘的方法。
While Massive Open Online Course (MOOCs) platforms provide knowledge in a new and unique way, the very high number of dropouts is a significant drawback. Several features are considered to contribute towards learner attrition or lack of interest, which may lead to disengagement or total dropout. The jury is still out on which factors are the most appropriate predictors. However, the literature agrees that early prediction is vital to allow for a timely intervention. Whilst feature-rich predictors may have the best chance for high accuracy, they may be unwieldy. This study aims to predict learner dropout early-on, from the first week, by comparing several machine-learning approaches, including Random Forest, Adaptive Boost, XGBoost and GradientBoost Classifiers. The results show promising accuracies (82%-94%) using as little as 2 features. We show that the accuracies obtained outperform state of the art approaches, even when the latter deploy several features.
