Cited By
View all- Beigman Klebanov BWeeks JSinharay S(2024)To Read or Not to Read: Predicting Student Engagement in Interactive ReadingArtificial Intelligence in Education10.1007/978-3-031-64299-9_15(209-222)Online publication date: 2-Jul-2024
Detecting the Disengaged Reader - Using Scrolling Data to Predict Disengagement during Reading
Authors: 
Daniel Biedermann, Jan Schneider, George-Petru Ciordas-Hertel, Beate Eichmann, Carolin Hahnel, Frank Goldhammer, Hendrik DrachslerAuthors Info & Claims
Daniel Biedermann, Jan Schneider, George-Petru Ciordas-Hertel, Beate Eichmann, Carolin Hahnel, Frank Goldhammer, Hendrik DrachslerAuthors Info & ClaimsPages 585 - 591
Abstract
When reading long and complex texts, students may disengage and miss out on relevant content. In order to prevent disengaged behavior or to counteract it by means of an intervention, it is ideally detected an early stage. In this paper, we present a method for early disengagement detection that relies only on the classification of scrolling data. The presented method transforms scrolling data into a time series representation, where each point of the series represents the vertical position of the viewport in the text document. This time series representation is then classified using time series classification algorithms. We evaluated the method on a dataset of 565 university students reading eight different texts. We compared the algorithm performance with different time series lengths, data sampling strategies, the texts that make up the training data, and classification algorithms. The method can classify disengagement early with up to 70% accuracy. However, we also observe differences in the performance depending on which of the texts are included in the training dataset. We discuss our results and propose several possible improvements to enhance the method.
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Index Terms
- Detecting the Disengaged Reader - Using Scrolling Data to Predict Disengagement during Reading
Index terms have been assigned to the content through auto-classification.
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March 2023
692 pages
ISBN:9781450398657
DOI:10.1145/3576050
- Program Chairs:
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- Hassan Khosravi,
- Bart Rienties,
- Shane Dawson
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Published: 13 March 2023
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LAK 2023
LAK 2023: 13th International Learning Analytics and Knowledge Conference
March 13 - 17, 2023
TX, Arlington, USA
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View all- Beigman Klebanov BWeeks JSinharay S(2024)To Read or Not to Read: Predicting Student Engagement in Interactive ReadingArtificial Intelligence in Education10.1007/978-3-031-64299-9_15(209-222)Online publication date: 2-Jul-2024
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