Suggesting a Teacher Assessment and Evaluation Model for Improving the Quality of English Teachers

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Yesilcinar S., Cakir A.

EGITIM VE BILIM-EDUCATION AND SCIENCE, vol.45, no.202, pp.363-392, 2020 (SSCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 45 Issue: 202
  • Publication Date: 2020
  • Doi Number: 10.15390/eb.2020.8463
  • Journal Indexes: Social Sciences Citation Index (SSCI), Scopus, EBSCO Education Source, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.363-392
  • Keywords: Teacher Assessment, Teacher Recruitment, English in Turkey, Consequential Validity, Washback Effect, FOREIGN-LANGUAGE, MIXED RESEARCH, PROFICIENCY, CONVERSATION, PERCEPTIONS, EXPERIENCES, SELECTION, PEDAGOGY, STUDENTS, SCIENCE
  • Gazi University Affiliated: Yes


Teacher quality is sine qua non of student achievement; however, choosing a qualified teacher is possible only when a valid and fair teacher assessment and evaluation (TAE) model is in place. The present mixed-method research aimed to develop a TAE model that can tell apart qualified English prospective teachers (who have required knowledge, skills, and attitudes) from unqualified ones. The qualitative data used in this research were collected from 78 stakeholders, whereas quantitative data were obtained from 271 inservice English teachers. Stakeholders' perceptions of testing were gathered as they are informative in terms of consequential validity of exams. The sample of prospective teachers and in-service teachers involved graduates of both English Language Teaching (ELT) and other departments such as English Language and Literature (ELL) since they could become English language teachers by obtaining a teacher certificate. Semi-structured and focus group interview techniques were used to obtain qualitative data. Regarding quantitative data, the English Teachers' Attitudes towards Recruitment System (ETARS), a valid and reliable scale developed by the researcher, was applied. SPSS was used to analyse quantitative data, and content analysis was used for qualitative data. The codes and categories were formed primarily by the researcher, and NVivo 12 was used to prevent data loss. Quantitative data showed that English teachers had a negative attitude towards the current TAE model. The qualitative findings were supported by quantitative data and indicated that the current TAE model is ineffective and unfair as it has both construct under-representation and construct-irrelevant variance (favouring graduates of ELL in terms of scoring). In this context, a new TAE model that can evaluate applicants' knowledge, skills, and attitudes has been developed. This model is considered to be helpful in selecting qualified teachers, thus improving the quality of foreign language education.