Applied Mathematics and Nonlinear Sciences
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Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 10, Issue 1


Published
on

September 26, 2025


Pages


DOI

Article

Analysis of Teaching Mode Innovation and Learning Effectiveness Assisted by Artificial Intelligence

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Authors

Haiying Luo Affiliation:
Zhanjiang University of Science and Technology, Zhanjiang, Guangdong, 524094, China.
and Yanhong Wu Affiliation:
Zhanjiang University of Science and Technology, Zhanjiang, Guangdong, 524094, China.


Abstract

In the context of artificial intelligence-assisted teaching, this paper constructs a deep knowledge tracking model (DKT-FC) that incorporates the Ebbinghaus forgetting curve. By analyzing students’ learning data and combining the theory of forgetting curve, it tracks students’ mastery of knowledge and forgetting more accurately. Afterwards, an intelligent teaching and learning support system was designed on this basis, and an innovative STSE-based teaching model that integrates science, technology, social and environmental education into the teaching process was constructed. Finally, the learning effectiveness under this model was clarified by covariance analysis and correlation analysis. The results showed that among the students in the experimental group, the correlation between the total score of co-ordination and co-ordination elaboration was the largest, with a correlation coefficient as high as 0.8579, and all the indicators showed a significant positive correlation with each other (P 0.05). The experimental results proved that the innovative teaching model of intelligent tutoring based on DKT-FC proposed in this paper has a good application effect in monitoring students’ learning effectiveness.


Keywords

Deep Knowledge Tracking Model (DKT-FC), STSE, Analysis of Covariance, Artificial Intelligence, 97B20


Citation

Luo, H. & Wu, Y. (2025). Analysis of teaching mode innovation and learning effectiveness assisted by artificial intelligence. Applied Mathematics and Nonlinear Sciences, 10(1). https://doi.org/10.2478/amns-2025-1071
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