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

<p>Based on subject knowledge mapping, this paper dynamically collects learning data, portrays learners’ learning situations, and accurately regulates the learning process. Personalized learning path recommendations and learning communities are constructed through learner profiling and learning services. Secondly, structural equation modeling was used to hypothesize the three-level elements of the E-GPPE-C model. Finally, 103 college students in smart teaching classes were taken as research subjects, and the utility of the smart teaching model was analyzed separately through the steps of precondition validation and cross-lag model with random intercepts. The results show that the smart teaching model has <italic>β</italic> =0.286 for deep learning strategy, <italic>β</italic> =0.211 for the smart classroom, and <italic>β</italic> =0.20 for classroom participation, and they accurately indicate that smart teaching has a positive facilitating mechanism on the learning ability of college students. This study also provides a useful reference for the practice of smart teaching in various disciplines in colleges and universities.</p>
</abstract>

Based on subject knowledge mapping, this paper dynamically collects learning data, portrays learners’ learning situations, and accurately regulates the learning process. Personalized learning path recommendations and learning communities are constructed through learner profiling and learning services. Secondly, structural equation modeling was used to hypothesize the three-level elements of the E-GPPE-C model. Finally, 103 college students in smart teaching classes were taken as research subjects, and the utility of the smart teaching model was analyzed separately through the steps of precondition validation and cross-lag model with random intercepts. The results show that the smart teaching model has β =0.286 for deep learning strategy, β =0.211 for the smart classroom, and β =0.20 for classroom participation, and they accurately indicate that smart teaching has a positive facilitating mechanism on the learning ability of college students. This study also provides a useful reference for the practice of smart teaching in various disciplines in colleges and universities.

Research on the Construction of Smart Teaching Mode with Artificial Intelligence Technology Facilitating Education Informatization in Colleges and Universities

Applied Mathematics and Nonlinear Sciences

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

Based on subject knowledge mapping, this paper dynamically collects learning data, portrays learners’ learning situations, and accurately regulates the...

Research on the Construction of Smart Teaching Mode with Artificial Intelligence Technology Facilitating Education Informatization in Colleges and Universities

Publié en ligne: 06 déc. 2023

Pages: -

Reçu: 23 déc. 2022

Accepté: 16 juin 2023

DOI: https://doi.org/10.2478/amns.2023.2.01409

Mots clés
Artificial intelligence, E-GPPE-C model, Knowledge graph, Learner profiling, Structural equation modeling

© 2023 Ying Yin et al., published by Sciendo

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

Research on the Construction of Smart Teaching Mode with Artificial Intelligence Technology Facilitating Education Informatization in Colleges and Universities

Publié en ligne: 06 déc. 2023

Pages: -

Reçu: 23 déc. 2022

Accepté: 16 juin 2023

DOI: https://doi.org/10.2478/amns.2023.2.01409

Mots clésArtificial intelligence, E-GPPE-C model, Knowledge graph, Learner profiling, Structural equation modeling

© 2023 Ying Yin et al., published by Sciendo

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

Mots clés
Artificial intelligence, E-GPPE-C model, Knowledge graph, Learner profiling, Structural equation modeling