Cluster Analysis of University Teacher’s Digital Knowledge and Competence Evaluation
DOI:
https://doi.org/10.65168/bs.218-5Keywords:
k-means, k-medoids, Outliers, Hopkins statistic, Mahalanobis distanceAbstract
The purpose of this study was to assess the level of university teachers’ digital competence based on student evaluations and to cluster teachers with similar ratings using clustering methods. A total of 514 students from first- to fifth-year courses at seven universities (MNUMS, MUST, MNDU, MNUFE, Etugen, Ider, and Ach) participated in the survey, providing evaluations through seven questions. Clustering analyses were conducted using the k-means and k-medoids (PAM) methods. The results showed high reliability and suitability of the dataset for clustering, as evidenced by Cronbach’s alpha (≥0.947), the Silhouette coefficient (≥0.88), and the Hopkins statistic (≥0.965). Both methods optimally classified teachers’ digital competence into two groups, “low” and “high,” with average scores ranging from 2.2–2.6 and 3.9–4.4, respectively. Compared to k-means, the k-medoids method provided more robust results due to its medoid-based approach and resilience to outliers. Moreover, 36.2–39.1% of students’ responses fell into the “low” competence cluster, while 60.9–63.8% were categorized as “high,” highlighting the potential to differentiate teachers’ digital competence levels. One-way ANOVA further confirmed statistically significant differences between the two clusters across all seven items (F > 300, p < 0.001), validating the distinct evaluation patterns. Overall, the study demonstrates that clustering methods, based on student evaluations, provide a reliable data-driven approach for identifying the digital competence levels of university teachers.
References
UNESCO, “Adverse Consequences of School Closures,” UNESCO, 2020.
R. Christine ба P. Yves, European framework for the digital competence of educators, Joint Research Centre (European Commission), 2017/11/26.
Piaget J., Grize J.B., Szeminska A., Bang V., Epistemology and Psychology of Functions, Dordrecht, Netherlands: D. Reidel Publishing Company, 1977.
Tondeur, J., Scherer, R., Siddiq, F., & Baran, E., “A comprehensive framework for teachers’ digital competence: Theoretical underpinnings and application,” Educational Technology Research and Development, б. 6, p. 1273–1290, 2017.
Tondeur, J., van Braak, J., Sang, G., Voogt, J., Fisser, P., & Ottenbreit-Leftwich,, “Technology integration in education: Evaluating the impact of professional development,” Educational Technology & Society, б. 59, %1-ийн д.д1, pp. 134-144, August 2012.
Romero-Rodríguez, J., Aznar-Diaz, I., Hinojo-Lucena, F., and Gomez-Garcia, G, “Mobile Learning in Higher Education: Structural Equation Model for Good Teaching Practices,” IEEE Access, б. 8, p. 91761–91769, 15 May 2020.
F, Han; A., Ellis R., “Personalised learning networks in the university blended learning context,” Comunicar, б. 28, pp. 19-30, 16 Oct 2019.
Moreno-Guerrero, A., Miaja-Chippirraz, N., Bueno-Pedrero, A., and Borrego-Otero, L., “The Information and Information Literacy Area of the Digital Teaching Competence,” б. 24, pp. 521-536, 2020.
V. K. Muhammed Murat Gümüş, “Developing a digital competence scale for teachers- validity and reliability study,” 28 June 2022.
I. M. Gómez-Trigueros, “Digital competence of higher education teachers and students: a systematic literature review (2011–2021),” Education and Information Technologies, б. 28, pp. 1981-2008, 2023.
T. Hastie, R. Tibshirani ба J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2, Хян., New York: Springer, 2009.
E. T. Service, iSkills™ Assessment: Measuring 21st Century ICT Literacy Skills, Princeton, NJ: ETS, 2007.
F. D. Davis, “Perceived usefulness, perceived ease of use, and user acceptance of information technology,” MIS Quarterly, б. 13, %1-ийн д.д3, pp. 319-340, 1989.
Үндэсний Статистикийн хороо, “Иргэдийн мэдээллийн технологийн хэрэглээний өнөөгийн байдал,” 2024. [Холбогдсон]. Available: https://sudalgaa.gov.mn/irgediyn-medeelliyn-tekhnologiyn-kheregleeniy-ngiyn-baydlyn-talaar-mzy.
Л. Ж. Ц.Ганбат, “Их сургуулийн оюутнуудын мэдээлэл, харилцаа холбооны технологийн хэрэглээнд хандах хандлагыг судлах нь. МУИС-ийн Боловсрол судлалын сэтгүүл,” Боловсрол судлал, б. 20, %1-ийн д.д1, pp. 45-52, 2019.
C. A. Sara Dias-Trindade, “University Teachers’ Digital Competence: A Case Study from Portugal,” 15 October 2022.
J. BRAIN HOPKINS, “A new method for determining the type of distribution of plant individuals,” Annals of Botany, б. 18, %1-ийн д.д2, pp. 213-227, 2 April 1954.
A. Banerjee ба R. Dave, “Validating clusters using the Hopkins statistic,” 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542), б. 1, pp. 149-153, 10 January 2004.
R. M. Cormack, “A Review of Classification,” Journal of the Royal Statistical A, б. 134, pp. 321-367, 1971.
P. J. Rousseeuw, “Silhouettes: A graphical aid to the interpretation and validation of cluster analysis,” Computational and Applied Mathematics, б. 20, pp. 53-65, 1987.
P. B. Catriel Beeri, “When is "nearest neighbor" meaningful?,” Jerusalem, Israel,, 1999.
“Who Belongs in the Family?,” The Psychometric Society, б. 18, %1-ийн д.д4, pp. 267-276, December 1953.
W. G. H. T. Tibshirani Robert, “Estimating the Number of Clusters in a Data Set Via the Gap Statistic,” Journal of the Royal Statistical Society Series B, p. 63, 01 January 2001.
L. Collins, “Research Design and Methods,” Encyclopedia of Gerontology (Second Edition), pp. 433-442, 12 February 2007.
Л. Ц.Мөнгөнтуул, “Оюутны сэтгэл ханамжийн үнэлгээний кластер шинжилгээ,” Эрүүл мэндийн шинжлэх ухаан, б. 19, %1-ийн д.д4, pp. 88-92, 2023.
Santiago Alonso-García, Juan José Victoria-Maldonado, Pablo José García-Sempere, Fernando Lara-Lara, “Student evaluation of teacher digitals skills at Granada University,” Frontiers in Education, б. 7, 2023.
Brian S. Everitt, Sabine Landau, Morven Leese, Daniel Stahl, Cluster Analysis, б. 1, 2011, p. 8.
