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<p>In the context of digitalizing big data and information, optimizing the teaching model of colleges and universities is a key concern in the education sector. This paper optimizes the shortest path planning of the Dijkstra algorithm around the Floyd algorithm and ant colony algorithm, constructs the model of a computer-aided teaching system in colleges and universities through the Dijkstra algorithm, and constructs the system model indexes from three aspects: students’ cognitive ability, students’ learning performance, and students’ learning interest. A sample of 100 college students was randomly selected, and the accuracy performance of the samples was compared by the Dijkstra algorithm and improved Dijkstra, respectively. The accuracy rate of the improved Dijkstra algorithm fluctuated from 83% to 90%, with a variable rate of 7%, which was relatively small. Then the Dijkstra algorithm was used to analyze the weighting of the cognitive ability index, learning achievement index, and learning interest index. In the cognitive ability index, the memory ability weight of 34% performed better compared with the other five, the good weight in the learning achievement index performed better compared with the other categories, and the more interesting weight in the learning interest index performed better overall compared with the other five categories. This study has guiding reference value for optimizing computer-assisted teaching mode in colleges and universities to improve teaching quality and efficiency of colleges and universities.</p>
</abstract>

In the context of digitalizing big data and information, optimizing the teaching model of colleges and universities is a key concern in the education sector. This paper optimizes the shortest path planning of the Dijkstra algorithm around the Floyd algorithm and ant colony algorithm, constructs the model of a computer-aided teaching system in colleges and universities through the Dijkstra algorithm, and constructs the system model indexes from three aspects: students’ cognitive ability, students’ learning performance, and students’ learning interest. A sample of 100 college students was randomly selected, and the accuracy performance of the samples was compared by the Dijkstra algorithm and improved Dijkstra, respectively. The accuracy rate of the improved Dijkstra algorithm fluctuated from 83% to 90%, with a variable rate of 7%, which was relatively small. Then the Dijkstra algorithm was used to analyze the weighting of the cognitive ability index, learning achievement index, and learning interest index. In the cognitive ability index, the memory ability weight of 34% performed better compared with the other five, the good weight in the learning achievement index performed better compared with the other categories, and the more interesting weight in the learning interest index performed better overall compared with the other five categories. This study has guiding reference value for optimizing computer-assisted teaching mode in colleges and universities to improve teaching quality and efficiency of colleges and universities.

Optimization of computer-assisted teaching mode in universities based on Dijkstra’s algorithm

Applied Mathematics and Nonlinear Sciences

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

In the context of digitalizing big data and information, optimizing the teaching model of colleges and universities is a key concern in the education sector....

Optimization of computer-assisted teaching mode in universities based on Dijkstra’s algorithm

Pubblicato online: 26 lug 2023

Pagine: -

Ricevuto: 04 set 2022

Accettato: 01 feb 2023

DOI: https://doi.org/10.2478/amns.2023.2.00088

Parole chiave
Big data, Dijkstra’s algorithm, Floyd’s algorithm, Ant colony algorithm, Assisted teaching system

© 2023 Li Yin, published by Sciendo

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

Optimization of computer-assisted teaching mode in universities based on Dijkstra’s algorithm

Pubblicato online: 26 lug 2023

Pagine: -

Ricevuto: 04 set 2022

Accettato: 01 feb 2023

DOI: https://doi.org/10.2478/amns.2023.2.00088

Parole chiaveBig data, Dijkstra’s algorithm, Floyd’s algorithm, Ant colony algorithm, Assisted teaching system

© 2023 Li Yin, published by Sciendo

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

Parole chiave
Big data, Dijkstra’s algorithm, Floyd’s algorithm, Ant colony algorithm, Assisted teaching system