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首页> 外文会议>International conference on intelligent tutoring systems >To Quit or Not to Quit: Predicting Future Behavioral Disengagement from Reading Patterns
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To Quit or Not to Quit: Predicting Future Behavioral Disengagement from Reading Patterns

机译：戒烟或不戒烟：根据阅读模式预测未来的行为脱离

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This research predicted behavioral disengagement using quitting behaviors while learning from instructional texts. Supervised machine learning algorithms were used to predict if students would quit an upcoming text by analyzing reading behaviors observed in previous texts. Behavioral disengagement (quitting) at any point during the text was predicted with an accuracy of 76.5% (48% above chance), before students even began engaging with the text. We also predicted if a student would quit reading on the first page of a text or continue reading past the first page with an accuracy of 88.5% (29% above chance), as well as if students would quit sometime after the first page with an accuracy of 81.4% (51% greater than chance). Both actual quits and predicted quits were significantly related to learning, which provides some evidence for the predictive validity of our model. Implications and future work related to ITSs are also discussed.
机译：这项研究通过在学习指导性文本的同时使用戒除行为来预测行为脱离。监督式机器学习算法用于通过分析先前课文中观察到的阅读行为来预测学生是否会退出下一课文。甚至在学生开始接触课文之前，可以预测课文中任何时候的行为脱离（退出）的准确性为76.5％（比机会高48％）。我们还预测了学生是否会退出文本的第一页或继续阅读第一页，其准确性为88.5％（比机会高出29％），以及学生是否会在第一页之后的某个时间退出并以准确率为81.4％（比机会大51％）。实际戒烟和预测戒烟都与学习显着相关，这为我们模型的预测有效性提供了一些证据。还讨论了与ITS相关的含义和未来的工作。

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