Precisely identifying and mitigating shallow reading behaviors through online reading data

Exploring quantitative reading behavior methods and precise behavioral intervention

Bibliographic Data

ID21632286
AuthorsYu Bai (0000-0003-1707-3081, Kyushu University), Jun Yi Shi (0000-0002-6879-2716, Media Science School, Northeast Normal University, Changchun, People's Republic of China), Fuzheng Zhao (0000-0002-3875-9819, Education Technology Center, Jilin University, Changchun, People's Republic of China), Etsuko Kumamoto (Information Science and Technology Center, Kobe University, Kobe, Japan), Chengjiu Yin (0000-0003-1492-5250, Research Institute for Information Technology, Kyushu University, Fukuoka, Japan, corresponding author)
Year2024
Volume33
Issue2
Pages1-15
Publication date2024-07-16
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueInteractive Learning Environments (JOURNAL)
Journal identifiersISSN: 1049-4820 • E-ISSN: 1744-5191
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/10494820.2024.2372846
OpenAlexW4400688554
LanguageEN
Citations received3
References cited23

Online learning has become a part of daily life. However, it has fostered a tendency to shallow reading. Notably, little attention has been paid to precisely identifying shallow reading behaviors using quantified education big data in favor of methods such as conceptual descriptions, opinions, and questionnaires, resulting in an imprecise analysis of its impact on learning and a lack of strategies to mitigate its causative factors. This study addresses the issue by introducing a behavior-identifying model grounded in measurable data to precisely identify shallow reading behaviors. Initially, three key dimensions of shallow reading behaviors were proposed, along with corresponding mathematical indicators. Subsequently, the k-means algorithm and Pearson correlation coefficient were employed to evaluate the relationship between shallow reading and learning performance, underscoring its significance. The analysis showed that shallow reading behaviors are highly associated with poor learning performance, thus hindering critical reading. These findings further validate the predominant influence of reading speed on learning performance. Consequently, a strategy was developed to intentionally decrease reading speed. Following that, an experiment was conducted to assess the effectiveness of this strategy in mitigating shallow reading. The results revealed improved learning performance and a significant reduction in shallow reading behaviors without increasing cognitive load

Multimedia · Reading motivation · Computer Science · Library Collection Development and Digital Resources · Psychology · Reading and Literacy Development · Technology Adoption and User Behaviour

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Open DOIOpen Access