Search
Warning: Undefined array key "6875//" in /web/zanos/classes/Edit/EditForm_class.php on line 263
Warning: Undefined array key "6875//" in /web/zanos/classes/Player/SearchArticle_class.php on line 261
Warning: Undefined array key "6875//" in /web/zanos/classes/Player/SearchArticle_class.php on line 261
# | Search | Downloads | ||||
---|---|---|---|---|---|---|
1 | The issue of assessing the level of professionalism and competencies of a teacher in secondary education is discussed in the paper and the concepts of “teacher professionalism”, “teacher competence”, “assessment technologies” are presented as well. Factors indicating the need to update approaches to assessing professionalism of teacher’s are indicated. Analysis of literature review on subject “teacher’s competence” has been carried out and connection between the level of teacher’s professionalism and level of his/her professional competencies has been established. The present-day assessment technologies have been analized and described. The technologies have been categorized into 4 groups according to the method of interaction, advantages of each group have been considered. The problem has been revealed, that is the above technologies do not consider the connection between the assessment of professionalism of teaching staff with students’ learning results, thus making assessment not complete and objective. The newly developed technology using cluster analysis for assessing competencies of teachers via analysis of students’ learning results of some subjects in Unified State Exam has been presented. The following stages of the technology implementation have been described: firstly, Dataset formation for the research of students’ learning results in different subjects in different educational institutions (secondary schools); calculation of average result for each subject under study; formation of a table of average results; implementation of clustering algorithms; sorting out educational institutions into clusters according to clustering results; analysis of final results of assessment procedures within each cluster and lastly determination of teachers’ deficits. The results of the implementation of the technology have been presented in the paper on the example of the results of Tomsk region educational organizations (secondary schools) in subjects “Russian”, “Mathematics (profile level)” within the period from 2015 to 2019. The findings revealed that more than 30 % of students could not cope the above exam assignments. Later the assignments were compared with the specifications of the USE test measuring materials and «complex for students» assignments were identified for each cluster. The research results make possible identification of teachers’ difficulties in teaching different subjects, and therefore formation of an individual teacher refresher training trajectory for enhancing professional skills, especially on the part of outlining and working out the needed competencies necessary for effective operating in class within one academic discipline, the latter ultimately should affect both personal professional development of teachers and quality growth of students’ learning results. Keywords: assessment technology, teacher competence, efficiency assessment, students’ learning results | 652 | ||||
2 | 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. Keywords: teacher competencies, clustering, teacher’s professional deficits, data-mining | 503 |