Modernization of the technology for assessing the professionalism and the level of competence of teachers based on the analysis of students’ learning results

An analysis of approaches, technologies, models developed over the past three years related to the assessment of professionalism and the level of competencies of teachers is presented. The attention is focused on research and development, which is based on working with big data, the use of technical means to automate the assessment process. An approach to assessing the professional competencies of teachers based on the analysis of the results of their students is formulated. Within the framework of this paper, the 3rd stage of the technology for assessing professionalism and competence level of general education teachers has been improved, tested and described. Using the data of the learning results of Tomsk region secondary students in the national Unified State Exam (USE), 3 (three) clustering algorithms were applied by technology: k-means, spectral clustering, agglomerative clustering. The learning results under study were on 4 (four) subjects: Russian, Mathematics (profile level), Physics, and Social Studies for the period from 2015 to 2019. The above data were divided into 2 (two) arrays: Science and Humanities, and clustering algorithms mentioned were applied to them. The validity of the clusters was assessed by the method of Silhouette coefficient and Kalinsky-Harabasz index. Some measured parameters of the algorithms and the expediency of their use within the framework of the task were evaluated.

data mining, teacher competencies, clustering, teacher’s professional deficits, data-mining