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

<p>This paper first proposes the direction of constructing a higher mathematics teaching mode supported by intelligent technology and then models the learner portrait of the learning outcome data according to the subject knowledge graph. An improved ant colony optimization algorithm is used to search for the optimal learning path, which is then combined with an improved convolutional neural network to generate a personalized learning path. The Trans R method is used to quantify the relationship between learners and learning resources and a semi-supervised learning conditional random field method based on K-NN is proposed to label learning resources and generate learning accurate evaluation for smart teaching. The smart teaching model of advanced mathematics courses is applied and analyzed in terms of students’ advanced mathematics pre and post-test scores, students’ satisfaction, teachers’ teaching methods and teaching resources in four directions. The analysis obtained that the posttest scores of the learners in the experimental group were 75.631, and the posttest scores of the learners in the control group were 66.314, with a difference of 9.317. The significance level of the variance chi-square test was 0.000&lt;0.05, which shows that there is a significant difference between the posttest scores of the experimental group and those of the control group, and it indicates that the wisdom teaching of higher mathematics significantly enhances the learning performance of the learners.</p>
</abstract>

This paper first proposes the direction of constructing a higher mathematics teaching mode supported by intelligent technology and then models the learner portrait of the learning outcome data according to the subject knowledge graph. An improved ant colony optimization algorithm is used to search for the optimal learning path, which is then combined with an improved convolutional neural network to generate a personalized learning path. The Trans R method is used to quantify the relationship between learners and learning resources and a semi-supervised learning conditional random field method based on K-NN is proposed to label learning resources and generate learning accurate evaluation for smart teaching. The smart teaching model of advanced mathematics courses is applied and analyzed in terms of students’ advanced mathematics pre and post-test scores, students’ satisfaction, teachers’ teaching methods and teaching resources in four directions. The analysis obtained that the posttest scores of the learners in the experimental group were 75.631, and the posttest scores of the learners in the control group were 66.314, with a difference of 9.317. The significance level of the variance chi-square test was 0.000<0.05, which shows that there is a significant difference between the posttest scores of the experimental group and those of the control group, and it indicates that the wisdom teaching of higher mathematics significantly enhances the learning performance of the learners.

An Innovative Model of Higher Mathematics Curriculum Education Incorporating Artificial Intelligence Technology

Applied Mathematics and Nonlinear Sciences

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

This paper first proposes the direction of constructing a higher mathematics teaching mode supported by intelligent technology and then models the learner...

An Innovative Model of Higher Mathematics Curriculum Education Incorporating Artificial Intelligence Technology

Online veröffentlicht: 18. Dez. 2023

Seitenbereich: -

Eingereicht: 14. Jan. 2023

Akzeptiert: 28. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01524

Schlüsselwörter
Artificial intelligence technology, Subject knowledge mapping, Ant colony optimization algorithm, Learning path, Higher mathematics course

© 2023 Xiaohui Zhang, published by Sciendo

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

An Innovative Model of Higher Mathematics Curriculum Education Incorporating Artificial Intelligence Technology

Online veröffentlicht: 18. Dez. 2023

Seitenbereich: -

Eingereicht: 14. Jan. 2023

Akzeptiert: 28. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01524

SchlüsselwörterArtificial intelligence technology, Subject knowledge mapping, Ant colony optimization algorithm, Learning path, Higher mathematics course

© 2023 Xiaohui Zhang, published by Sciendo

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

Schlüsselwörter
Artificial intelligence technology, Subject knowledge mapping, Ant colony optimization algorithm, Learning path, Higher mathematics course