Using Machine Learning to Assess Preschoolers’ Competencies by Region
DOI:
https://doi.org/10.56380/mjer.v31.1.5Keywords:
HCPC, PCA, MCA, MFA, Machine LearningAbstract
Competencies by Region This article is based on a study of preschoolers’ developmental progress, local and regional. Here, based on the task of assessing mathematical competencies in the curriculum, the mathematical competencies of 3-5 year-old participants were compared using machine learning methods and methodologies in terms of age and regional classification, calculation, similarity, common and specific features. By analyzing these factors, it can be seen that the competencies of the participants vary depending on age and region. The study sample was held among 450 participants from the “Preschool Kindergarten”. From all this, it can be seen that the evaluation of the competencies of the total participants, the evaluation of the curriculum, the classification of age and region, the common and specific features, and the similarity of the assessments. The use of machine learning methods and methodologies in educational databases and training systems will be compared in terms of the quality of training, the assessment of the competencies of the participants, and the classification of age and region. Therefore, it is important for teachers, educators, and policymakers to evaluate and report on their work.
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References
Constantin, 2. #. (2021. 28). Distributing participation int design: Addressing challenges of a global pandemic. Int J Child Computer Interact, p. 100255.
Documentation, R. (2020). HCPC: Hierarchical Clustering on Principle Components (HCPC).
Giannakos, M. (. (2022). Learning Technology and Child – Computer Interaction. In Experimental Studies in Learning Technology and Child – Computer Interaction . Cham: Springer International Publishing, (pp. 7-14).
Giovanni Troiano. (2019). Is My Game OK Dr. Scratch?: Exploring Programming and Computational Thinking Development via Metrics in Students – Designed Serious Games for STEM. . The Interaction Design and Children, DOI:10.1145/3311927.3323152.
Haiyan Bai, X. L. (2022). Machine Learning Method for High Dimensional Education Data. Journal of Methods and Measurement in the Social Sciences, Vol. 13, No.1, 1-14,.
Han, J. K. (2011). Data mining: concepts and techniques. S.-S. t. f. h.-t. d. analysis. . Retrieved from HCPC - Hierarchical Clustering on Principal Components .
Jing Yang, Z. W. (2022). Interface Design Evaluation of Educational APP for Pre-School Children Based on Analytic Hierarchy Process. DOI: 10.54941/ahfe1001724.
Jing Zhang. (2025). Machine Learning Based Big Data Analytics for Education in Curriculum Reforms,. Applied Mathematics and Nonlinear Sciences 10(1),, DOI:10.2478/amns-2025-0135.
John F. Pane. (2002). A Programming System for Children that is Designed for Usability.
Lukasz Kidzinski. (2015). A Tutorial on Machine Learning in Educational Science. The 2nd International Conference on Smart Learning Environments, (pp. DOI:10.1007/978-981-287-868-7_54).
Ministry of education and science. (2019). Preschool Curriculum and Recommendations for Implementation. UB, Mongolia.
N., A. (2020). Child–Computer Interaction in times of a pandemic. Christopher Frauenberger. , https://doi.org/10.1016/j.ijcci.2020.1002012212-8689.
Román – Gonzálezв. (2017). Which cognitive abilities underlie computational thinking? Criterion validity of the Computational Thinking Test. . Computers in Human Behavior 72(2):678–691, DOI:10.1016/j.chb.2016.08.047.
(n.d.). Retrieved from https://www.eec.mn .
Company, 8. (2021). Retrieved from https://www.eec.mn
Company, G. (2021). Retrieved from https://www.legalinfo.mn
Company, I. (2021). Unified legal information system. Retrieved from https://www.legalinfo.mn
