On the Effectiveness of Self-Training in MOOC Dropout Prediction
On the Effectiveness of Self-Training in MOOC Dropout Prediction
复制标题
论自我训练在 MOOC 辍学预测中的有效性
作者：
Yamini Goel;Rinkaj Goyal
摘要近年来，大规模开放式在线课程（MOOC）获得了巨大的普及，吸引了全球各地的学习者。然而，MOOC面临着高辍学率的关键挑战，辍学率在91%-93%之间。不同的学习分析策略和MOOC之间的相互作用已经成为降低辍学率的研究领域。大多数现有的研究使用点击流功能作为参与模式来预测有风险的学生。然而，这项研究使用的点击流功能和学习者的朋友的影响力的基础上，他们的人口统计数据，以确定潜在的辍学。现有的预测模型基于监督学习技术，需要大量手工标记的数据来训练模型。然而，在实践中，大量标记数据的稀缺使得训练变得困难。因此，本研究使用自我训练（一种半监督学习模型）来开发预测模型。在公共数据集上的实验结果表明，半监督模型获得了与最先进方法相当的结果，同时还具有利用少量标记数据的灵活性。本研究使用了七个著名的优化器来训练自训练分类器，其中随机梯度下降（SGD）的F1得分为94.29%，肯定了本文的相关性。
Abstract Massive open online courses (MOOCs) have gained enormous popularity in recent years and have attracted learners worldwide. However, MOOCs face a crucial challenge in the high dropout rate, which varies between 91%-93%. An interplay between different learning analytics strategies and MOOCs have emerged as a research area to reduce dropout rate. Most existing studies use click-stream features as engagement patterns to predict at-risk students. However, this study uses a combination of click-stream features and the influence of the learner’s friends based on their demographics to identify potential dropouts. Existing predictive models are based on supervised learning techniques that require the bulk of hand-labelled data to train models. In practice, however, scarcity of massive labelled data makes training difficult. Therefore, this study uses self-training, a semi-supervised learning model, to develop predictive models. Experimental results on a public data set demonstrate that semi-supervised models attain comparable results to state-ofthe-art approaches, while also having the flexibility of utilizing a small quantity of labelled data. This study deploys seven well-known optimizers to train the self-training classifiers, out of which, Stochastic Gradient Descent (SGD) outperformed others with the value of F1 score at 94.29%, affirming the relevance of this exposition.