论文标题
在多模式在线环境中识别风险的K-12学生：一种机器学习方法
Identifying At-Risk K-12 Students in Multimodal Online Environments: A Machine Learning Approach
论文作者
论文摘要
随着K-12在线学习平台的迅速出现，教育的新时代已经开放。有一个辍学警告框架至关重要的是，先发制人地识别有可能退出在线课程的K-12学生。先前的研究人员专注于预测大规模开放在线课程（MOOC）的辍学，这些课程通常提供高等教育，即高等教育的研究生水平课程。但是，很少有研究重点是为K-12在线课程中的学生开发机器学习方法。在本文中，我们开发了一个机器学习框架，以进行精确的高危学生识别，专门针对K-12多模式在线环境。我们的方法考虑了围绕K-12学生的在线和离线因素，旨在解决（1）多种方式的挑战，即K-12在线环境涉及来自视频，语音等不同方式的互动； （2）长度可变性，即学习历史长度不同的学生； （3）时间敏感性，即，辍学的可能性随着时间而变化； （4）数据不平衡，即，只有少于20 \％的K-12学生会选择退学。我们进行了广泛的离线和在线实验，以证明我们方法的有效性。在我们的离线实验中，我们表明我们的方法可以改善与现实世界教育数据集中的最新基线相比，提高了辍学的预测性能。在我们的在线实验中，我们在第三方K-12在线辅导平台上测试了两个月的方法，结果表明，系统检测到70％以上的辍学学生。
With the rapid emergence of K-12 online learning platforms, a new era of education has been opened up. It is crucial to have a dropout warning framework to preemptively identify K-12 students who are at risk of dropping out of the online courses. Prior researchers have focused on predicting dropout in Massive Open Online Courses (MOOCs), which often deliver higher education, i.e., graduate level courses at top institutions. However, few studies have focused on developing a machine learning approach for students in K-12 online courses. In this paper, we develop a machine learning framework to conduct accurate at-risk student identification specialized in K-12 multimodal online environments. Our approach considers both online and offline factors around K-12 students and aims at solving the challenges of (1) multiple modalities, i.e., K-12 online environments involve interactions from different modalities such as video, voice, etc; (2) length variability, i.e., students with different lengths of learning history; (3) time sensitivity, i.e., the dropout likelihood is changing with time; and (4) data imbalance, i.e., only less than 20\% of K-12 students will choose to drop out the class. We conduct a wide range of offline and online experiments to demonstrate the effectiveness of our approach. In our offline experiments, we show that our method improves the dropout prediction performance when compared to state-of-the-art baselines on a real-world educational dataset. In our online experiments, we test our approach on a third-party K-12 online tutoring platform for two months and the results show that more than 70\% of dropout students are detected by the system.
