Science for Education Today, 2020, vol. 10, no. 6, pp. 220–236
© Novopashina L. A., Grigoryeva E. G., Kuzina D. V., Cherkasova Y. A., 2020
UDC: 
378.2, 378.046.4, 37.08

The capabilities of mathematical modeling tools for the system of assessing teacher shortages

Novopashina L. A. 1 (Krasnoyarsk, Russian Federation), Grigoryeva E. G. 1 (Krasnoyarsk, Russian Federation), Kuzina D. V. 1 (Krasnoyarsk, Russian Federation), Cherkasova Y. A. 1 (Krasnoyarsk, Russian Federation)
1 Krasnoyarsk state pedagogical University named after V. P. Astafiev
Full text (Open Access) — Downloaded 995
Abstract: 

Introduction. The article provides an overview and examines the problem of reliability and validity of models and tools for assessing teacher shortages. The purpose of the article is to determine the capabilities of mathematical modeling tools for the system of assessing teacher shortages.
Materials and Methods. The research is based on measuring students’ intentions to enter the teaching profession using mathematical modeling tools. The main methodological principle of the research is a systematic approach. Based on the data obtained from a survey of 1,797 undergraduate and postgraduate students at Krasnoyarsk State Pedagogical University named after V. P. Astafiev, the method of multiple regression analysis is used.
Results. The main results are that the authors investigated the capabilities of using mathematical modeling tools to assess teacher shortages (multiple regression analysis). It was found that the decision of teacher education students to enter the teaching profession does not depend on their socio-demographic characteristics (gender, year of study, place of residence, field of study, etc.) and is directly related to the characteristics of students’ attitudes to the University, their chosen profession, and professional development. There is a direct dependence of plans to start a teaching career on the self-assessment of their choice of the university. The probability of choosing a teaching career is determined by the ideas of professional development and is directly related to ‘improving skills in the subject area’; ‘mastering meta-competencies: analyzing and reflecting on their activities, evaluating and presenting results, etc.’, ‘mastering new techniques’, ‘transferring knowledge and skills to others, sharing experience’. The authors identified the capacity of using multiple regression analysis for assessing teacher shortages. The authors found that in solving this type of problems, it is reasonable to use empirical mathematical models, which include multiple regression analysis.
Conclusions. The article concludes that the mathematical model of multiple regression analysis is a tool for comprehensive assessment. The authors note that multiple regression analysis, as a method of mathematical modeling, allows evaluating the activity in the unity of all characteristics and can be used in the assessment of teacher shortages. The data obtained serve as basic indicators and are used to establish links and relationships between choosing a teacher education degree programme and the intention to pursue a teaching career.

Keywords: 

Assessment systems; Assessment tools; Mathematical modeling; Teachers shortages; Students' intentions.

URL WoS/RSCI: https://www.webofscience.com/wos/rsci/full-record/RSCI:44478890

Prominence Percentile SciVal: 79.876 Teaching Profession | Career Choice | Teacher Motivation

https://www.scopus.com/record/display.uri?eid=2-s2.0-85099470482&origin=...

The capabilities of mathematical modeling tools for the system of assessing teacher shortages

For citation:
Novopashina L. A., Grigoryeva E. G., Kuzina D. V., Cherkasova Y. A. The capabilities of mathematical modeling tools for the system of assessing teacher shortages. Science for Education Today, 2020, vol. 10, no. 6, pp. 220–236. DOI: http://dx.doi.org/10.15293/2658-6762.2006.12
References: 
  1. Vorobyeva M. A. Evaluation of teachers in the educational organization. Pedagogical Education in Russia, 2017, no. 7, pp. 45–50. URL:  https://www.elibrary.ru/item.asp?id=29861647
  2. Curado М. А., Teles J., Marôco J, Analysis of variables that are not directly observable: influence on decision-making during the research process. Revista da Escola de Enfermagem da USP, 2014, vol. 48 (1), pp. 146–152. DOI: http://dx.doi.org/10.1590/S0080-623420140000100019  
  3. Ermeling, B. A., Yarbo, J. Expanding instructional horizons: A case study of teacher team-outside expert partnership. Teachers Colleges Record, 2016, vol. 118 (2), pp. 1–48. URL:  https://eric.ed.gov/?id=EJ1087008
  4. King F. Teacher professional development to support teacher professional learning: Systemic factors from Irish case studies. Teacher Development, 2016, vol. 20 (4), pp. 574–594. DOI:  https://doi.org/10.1080/13664530.2016.116166  
  5. Temnyatkina O., Tokmeninova D. Modern approaches to teacher performance assessment an overview of foreign publications. Educational Studies. Moscow, 2018, no. 3, pp. 180–195.  DOI: https://doi.org/10.17323/1814-9545-2018-3-180-195 URL: https://www.elibrary.ru/item.asp?id=35618161  
  6. Temnyatkina O., Tokmeninova D. Models of teacher performance evaluation applied in various countries. Perspectives of Science and Education, 2019, no. 3, pp. 489–499. DOI: https://doi.org/10.32744/pse.2019.3.37   URL:  https://www.elibrary.ru/item.asp?id=38545623    
  7. Trškan D. Quality indicators, a new method for the evaluation of teaching practice in teacher education programmes in Slovenia: An example of quality indicators for school mentors – external mentors. Revista Electrónica Interuniversitaria de Formación del Profesorado, 2017, vol. 20 (2), pp. 63–77. DOI: http://dx.doi.org/10.6018/reifop.20.1.273871   
  8. Grau V., Calcagni E., Preiss D. D., Ortiz D. Teachers’ professional development through university-school partnerships: Theoretical standpoints and evidence from two pilot studies in Chile. Cambridge Journal of Education, 2017, vol. 47 (1), pp. 19–36. DOI: https://doi.org/10.1080/0305764X.2015.1102867   
  9. Scheer E. A., Bijlsma H. J. E., Glas C. A. W. Validity and reliability of student perceptions of teaching quality in primary education. School Effectiveness and School Improvement, 2019, vol.  30  (1), pp. 30–50, DOI: http://dx.doi.org/10.1080/09243453.2018.1539015
  10. Novopashina L. A. Teachers' professionalguiding principles in pedagogical university. Bulletin of the Krasnoyarsk State Pedagogical University named after V. P. Astafiev, 2020, no. 1, pp. 114–129. DOI: http://dx.doi.org/10.25146/1995-0861-2020-51-1-189   URL: https://www.elibrary.ru/item.asp?id=42599792  
  11. Petunin O. V. Teacher professional difficulties when implementing the federal state educational standards for general education. Modern Problems of Science and Education, 2016, no. 1, pp. 40. URL: https://www.elibrary.ru/item.asp?id=25509287
  12. Griffin P., Murray L., Care E., Thomas A., Perri P. Developmental assessment: Lifting literacy through professional learning teams. Assessment in Education: Principles. Policy and Practice, 2010, vol. 17 (4), pp. 383–397. DOI:  https://doi.org/10.1080/0969594.2010.516628
  13. Cravens X., Wang J. Learning from the masters: Shanghai’s teacher-expertise infusion system. International Journal for Lesson and Learning Studies, 2017, vol. 6 (4), pp. 306–320. DOI: http://dx.doi.org/10.1108/IJLLS-12-2016-0061
  14. Su Y., Feng L., Hsu C. Accountability or authenticity? The alignment of professional development and teacher evaluation. Teachers and Teaching, 2017, vol. 23 (6), pp. 717–728. DOI: https://doi.org/10.1080/13540602.2016.1255189  
  15. Fedorov O. D., Zhuravleva O. N., Polyakova T. N. Educational strategemes in the design of supplementary professional teacher education programs: setting the priorities. Educational Studies Moscow, 2018, no. 2, pp. 71‒90. DOI: https://doi.org/10.17323/1814-9545-2018-2-71-90  URL: https://www.elibrary.ru/item.asp?id=35203139  
  16. Kremen S. A., Kremen F. M. Humeniies alumini as a potential participants of labor market. Social Education, 2016, no. 1, pp. 41–46. URL: https://www.elibrary.ru/item.asp?id=25316257
  17. Grigorieva E. G., Novopashina L. A., Bocharova Y. Y. Socio-demographic and professional characteristics of academic staff at a regional university. Science for Education Today, 2019, vol.  9  (2), pp. 170–187. DOI: http://dx.doi.org/10.15293/2658-6762.1902.12  URL: https://www.elibrary.ru/item.asp?id=38191472    
  18. Griffin P., Murray L., Care E., Thomas A., Perri P. Developmental assessment: Lifting literacy through professional learning teams. Assessment in Education: Principles. Policy and Practice, 2010, vol. 17 (4), pp. 383–397. DOI:  https://doi.org/10.1080/0969594.2010.516628  
  19. Gorb V. G. Professional standard of a teacher: methodological uncertainties and ways to overcome them. Innovative Projects and Programs in Education, 2016, no. 1, pp. 6–13. URL: https://www.elibrary.ru/item.asp?id=29313280  
  20. Bershadskaya E. A., Bershadsky M. E. methods for identifying professional deficits in teachers at schools with low learning outcomes and schools operating in difficult social conditions. Municipal Education: Innovations and Experiment, 2017, no. 2, pp. 41–51. URL: https://www.elibrary.ru/item.asp?id=28914553  
Date of the publication 31.12.2020