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Comparative Analysis of Teachers' Digital Knowledge Competence Assessment Using K-Means and K-Medoids Clustering
Зохиогч
Он
2025 оны есдүгээр сарын 16
Төрөл
conference paper
Start Page
1
End Page
6
Хураангуй
The primary objective of this study was to identify the level of digital competence among university lecturers based on student evaluations and to group lecturers with similar evaluation patterns using clustering analysis. A total of 514 undergraduate students from seven public and private higher education institutions in Mongolia-namely, Mongolian university of science and technology (MUST), Mongolian National University of Medical Sciences (MNUMS), Mongolian State University of Education (MSUE), ETUGEN, IDER, and ACH Universities-participated in the study. The data, comprising responses to seven evaluation items, were analyzed using K-Means and K-Medoids (PAM algorithm) clustering methods. Key statistical indicators such as Cronbach's alpha$(\geq 0.947)$, the Silhouette coefficient$(\geq 0.88)$, and the Hopkins statistic$(\geq 0.965)$confirmed the reliability of the questionnaire and the suitability of the data for clustering. Both clustering techniques effectively categorized lecturers into two distinct groups labeled as “Average” and “Good,” with average ratings ranging from 2.2-2.6 and 3.9-4.4, respectively. The K-Medoids method, being medoid-based and more robust to outliers, produced more reliable results. Between 36.2% and 39.1% of students were grouped under the “Average” category, while 60.9 % to 63.8 % fell under the “Good” category, indicating a strong potential for differentiating lecturers' digital competencies. One-way ANOVA revealed statistically significant differences ($\mathrm{F}>300, \mathrm{p}<0.001$) between the two clusters for each of the seven evaluation items (A1-A7), confirming the divergence in student assessment patterns. This study demonstrates that student feedback can serve as a credible basis for assessing university lecturers' digital competence through robust, data-driven methods.