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Predicting student dropout in subscription-based online learning environments: The beneficial impact of the logit leaf model

机译：预测学生辍学在订阅的在线学习环境中：Logit叶模型的有益影响

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摘要

Online learning has been adopted rapidly by educational institutions and organizations. Despite its many advantages, including 24/7 access, high flexibility, rich content, and low cost, online learning suffers from high dropout rates that hamper pedagogical and economic goal outcomes. Enhanced student dropout prediction tools would help providers proactively detect students at risk of leaving and identify factors that they might address to help students continue their learning experience. Therefore, this study seeks to improve student dropout predictions, with three main contributions. First, it benchmarks a recently proposed logit leaf model (LLM) algorithm against eight other algorithms, using a real-life data set of 10,554 students of a global subscription-based online learning provider. The LLM outperforms all other methods in finding a balance between predictive performance and comprehensibility. Second, a new multilevel informative visualization of the LLM adds novel benefits, relative to a standard LLM visualization. Third, this research specifies the impacts of student demographics; classroom characteristics; and academic, cognitive, and behavioral engagement variables on student dropout. In reviewing LLM segments, these results show that different insights emerge for various student segments with different learning patterns. This notable result can be used to personalize student retention campaigns.
机译：在线学习被教育机构和组织迅速采用。尽管有许多优势，包括全天候获得的服务，高度灵活性，丰富的内容，以及低成本，在线学习遭受了妨碍教学和经济目标结果的高辍学率。增强的学生丢失预测工具将帮助提供商积极地检测有关危险的学生，并确定他们可能会解决这些因素，以帮助学生继续学习经验。因此，本研究旨在提高学生辍学预测，具有三个主要贡献。首先，它将最近提出的Logit叶模型（LLM）算法用于八个其他算法，使用10,554名基于在线学习提供商的10,554名学生的现实生活数据集。 LLM优于在预测性能和可理解性之间找到平衡的所有其他方法。其次，相对于标准LLM可视化，LLM的新多级信息可视化增加了新颖的益处。第三，本研究规定了学生人口统计学的影响;课堂特征;学生辍学的学术，认知和行为接触变量。在审查LLM段时，这些结果表明，不同的学生段具有不同学习模式的不同洞察力。这个值得注意的结果可用于个性化学生保留运动。

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