@inproceedings{e5ba9c59f2e64a9981f20db873d022b2,
title = "To quit or not to quit: Predicting future behavioral disengagement from reading patterns",
abstract = "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.",
keywords = "ITSs, affect detection, disengagement, engagement, reading",
author = "Caitlin Mills and Nigel Bosch and Art Graesser and Sidney D'Mello",
year = "2014",
doi = "10.1007/978-3-319-07221-0_3",
language = "English (US)",
isbn = "9783319072203",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "19--28",
booktitle = "Intelligent Tutoring Systems - 12th International Conference, ITS 2014, Proceedings",
note = "12th International Conference on Intelligent Tutoring Systems, ITS 2014 ; Conference date: 05-06-2014 Through 09-06-2014",
}