Exploring Pedagogical Dynamics via Teachers' Interactions with Digital Textbook Platforms.
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| Title: | Exploring Pedagogical Dynamics via Teachers' Interactions with Digital Textbook Platforms. |
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| Authors: | Xie, Tao (AUTHOR), Li, Yan (AUTHOR), Dai, Yi (AUTHOR) |
| Source: | International Journal of Human-Computer Interaction. Jun2025, Vol. 41 Issue 12, p7873-7883. 11p. |
| Subjects: | Teacher development, Recurrent neural networks, Digital technology, Teachers, Artificial intelligence, Electronic textbooks |
| Abstract: | Understanding the pedagogical dynamics that occur when teachers interact with digital textbook devices (DTDs) in the artificial intelligence (AI)–enhanced teaching environment is critical for education success. Previous research on the use of DTDs has primarily focused on subjective and intrusive questionnaire surveys, with little attention paid to the accompanying data when teachers use AI tools. This study aims to mine teachers' behaviors from a data-driven perspective, exploring actual practices as they integrate DTDs into their teaching. We collected teachers' behavioral data from a cloud-based curriculum platform, covering four subjects over a four-month period. Then, we used the lag sequential analysis (LSA) technique to investigate state-change patterns and the motif discovery technique to uncover hidden sequential patterns in teachers' behaviors. Furthermore, using two mutually dependent recurrent neural networks, we predicted teachers' future behaviors while operating DTDs, providing a more complete understanding of their pedagogical dynamics. Our findings show significant behavioral patterns when teachers use AI–embedded digital devices in the smart teaching environment. Additionally, the predictive model performed well in predicting teachers' future behaviors, which can be used to improve the design of human–computer interaction in digital textbooks. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | Understanding the pedagogical dynamics that occur when teachers interact with digital textbook devices (DTDs) in the artificial intelligence (AI)–enhanced teaching environment is critical for education success. Previous research on the use of DTDs has primarily focused on subjective and intrusive questionnaire surveys, with little attention paid to the accompanying data when teachers use AI tools. This study aims to mine teachers' behaviors from a data-driven perspective, exploring actual practices as they integrate DTDs into their teaching. We collected teachers' behavioral data from a cloud-based curriculum platform, covering four subjects over a four-month period. Then, we used the lag sequential analysis (LSA) technique to investigate state-change patterns and the motif discovery technique to uncover hidden sequential patterns in teachers' behaviors. Furthermore, using two mutually dependent recurrent neural networks, we predicted teachers' future behaviors while operating DTDs, providing a more complete understanding of their pedagogical dynamics. Our findings show significant behavioral patterns when teachers use AI–embedded digital devices in the smart teaching environment. Additionally, the predictive model performed well in predicting teachers' future behaviors, which can be used to improve the design of human–computer interaction in digital textbooks. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 10447318 |
| DOI: | 10.1080/10447318.2024.2400413 |
