<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Digital reading has changed the traditional reading pattern, and it is popular among college students because of its portability, fast content update and rich presentation, but the phenomenon of shallow reading such as lack of concentration, lack of thinking and superficiality in the process of reading is also becoming more and more obvious. In this paper, we construct the model of college students’ reading ability based on RoBTANet model, construct the index system of college students’ reading ability evaluation based on the relationship structure between the main categories derived from the rooted coding, and finally design the model of college students’ reading ability evaluation to achieve the purpose of analyzing the results of college students’ reading ability evaluation. The scoring results correspond to the data of 59 initial concepts, 27 valid indicators, and 5 main categories of a student, which can be mapped to the reading ability of students through the various scores and provide effective countermeasures for the digital reading mode of college students. This paper effectively evaluates the reading ability of college students, broadens the application value and research field of RoBTANet model, actively explores the digital reading mode of college students, provides development ideas, and is important to promote the innovation and application of digital reading mode of college students.</p>
</abstract>

Digital reading has changed the traditional reading pattern, and it is popular among college students because of its portability, fast content update and rich presentation, but the phenomenon of shallow reading such as lack of concentration, lack of thinking and superficiality in the process of reading is also becoming more and more obvious. In this paper, we construct the model of college students’ reading ability based on RoBTANet model, construct the index system of college students’ reading ability evaluation based on the relationship structure between the main categories derived from the rooted coding, and finally design the model of college students’ reading ability evaluation to achieve the purpose of analyzing the results of college students’ reading ability evaluation. The scoring results correspond to the data of 59 initial concepts, 27 valid indicators, and 5 main categories of a student, which can be mapped to the reading ability of students through the various scores and provide effective countermeasures for the digital reading mode of college students. This paper effectively evaluates the reading ability of college students, broadens the application value and research field of RoBTANet model, actively explores the digital reading mode of college students, provides development ideas, and is important to promote the innovation and application of digital reading mode of college students.

Innovation and application of digital reading model for college students based on deep learning model

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Digital reading has changed the traditional reading pattern, and it is popular among college students because of its portability, fast content update and rich...

Innovation and application of digital reading model for college students based on deep learning model

Online veröffentlicht: 02. Juni 2023

Seitenbereich: -

Eingereicht: 27. Apr. 2022

Akzeptiert: 05. Okt. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00130

Schlüsselwörter
Deep learning models. Digital reading, RoBTANet model, Evaluation index system, Rooted coding theory

© 2023 Li Yuan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Innovation and application of digital reading model for college students based on deep learning model

Online veröffentlicht: 02. Juni 2023

Seitenbereich: -

Eingereicht: 27. Apr. 2022

Akzeptiert: 05. Okt. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00130

SchlüsselwörterDeep learning models. Digital reading, RoBTANet model, Evaluation index system, Rooted coding theory

© 2023 Li Yuan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Deep learning models. Digital reading, RoBTANet model, Evaluation index system, Rooted coding theory