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

<p>In the realm of higher education management, data mining has emerged as a potent tool for elevating the quality of instruction and administrative operations. This study presents the development of a curriculum validation analysis model utilizing association rule clustering. We collected and processed data from design students at a university, addressing missing values and eliminating outliers. Through data mining, we explored the relationships between students’ coursework, skill acquisition, and the demands of potential employers in society. This analysis underpinned a series of reforms in higher education curricula. To assess the impact of these curricular changes, two instructors conducted both process and outcome evaluations of the revised programs. The evaluations revealed that five student groups achieved scores above 80, one group scored between 70 and 80, and three groups scored between 60 and 70. All student groups scored above 60, demonstrating their competence in fundamental job-related tasks and validating their readiness for professional employment. This research offers valuable insights for the revision of professional training programs and curriculum reform in higher education.</p>
</abstract>

In the realm of higher education management, data mining has emerged as a potent tool for elevating the quality of instruction and administrative operations. This study presents the development of a curriculum validation analysis model utilizing association rule clustering. We collected and processed data from design students at a university, addressing missing values and eliminating outliers. Through data mining, we explored the relationships between students’ coursework, skill acquisition, and the demands of potential employers in society. This analysis underpinned a series of reforms in higher education curricula. To assess the impact of these curricular changes, two instructors conducted both process and outcome evaluations of the revised programs. The evaluations revealed that five student groups achieved scores above 80, one group scored between 70 and 80, and three groups scored between 60 and 70. All student groups scored above 60, demonstrating their competence in fundamental job-related tasks and validating their readiness for professional employment. This research offers valuable insights for the revision of professional training programs and curriculum reform in higher education.

Convergence of Artificial Intelligence Algorithms and Education Fuels Educational Reform in Higher Education Programs

Applied Mathematics and Nonlinear Sciences

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

In the realm of higher education management, data mining has emerged as a potent tool for elevating the quality of instruction and administrative operations....

Convergence of Artificial Intelligence Algorithms and Education Fuels Educational Reform in Higher Education Programs

Online veröffentlicht: 30. Mai 2024

Seitenbereich: -

Eingereicht: 15. Feb. 2024

Akzeptiert: 27. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1267

Schlüsselwörter
Association rule clustering, Similarity measures, Systematic clustering method, Curriculum reforms

© 2024 Jingjing Nie, published by Sciendo

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

Convergence of Artificial Intelligence Algorithms and Education Fuels Educational Reform in Higher Education Programs

Online veröffentlicht: 30. Mai 2024

Seitenbereich: -

Eingereicht: 15. Feb. 2024

Akzeptiert: 27. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1267

SchlüsselwörterAssociation rule clustering, Similarity measures, Systematic clustering method, Curriculum reforms

© 2024 Jingjing Nie, published by Sciendo

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

Schlüsselwörter
Association rule clustering, Similarity measures, Systematic clustering method, Curriculum reforms