An Evaluation of the Teaching-Learning Functions of Teachers in Yazd Elementary Schools Based on Assure Model and Demographic Characteristics

Document Type : Original Article

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Abstract

The goal of the present study is to evaluate the teaching-learning functions of teachers in Yazd elementary schools based on Assure model and demographic characteristics. This study is conducted through applied research and the research method is descriptive-survey. The research community include all elementary school teachers in Yazd in the academic year of 2016-2017 (n=4196). Using the Krejcie and Morgan table (95% confidently), the sample population is estimated as 351 individuals that are selected through a proportional volume method. The data collection tools include a researcher-made questionnaire developed based on Assure model.  The statistical methods in this research are descriptive statistics (mean, frequency distribution and standard deviation) and inferential statistics (T-test single-sample, T-test, two-group independent, F-test, one-way analysis of variance and related follow-up tests). The obtained results show that the average functions of Yazd elementary teachers in dimensions such as analysis of students’ characteristics and in choosing methods, media, teaching aids for training are lower than the average and in dimensions such as using media and teaching aids in teaching process is at the average levels. While in dimensions such as expressing the educational goals and students’ participation rate and evaluating and revising the teaching process they show above the average functions. The teachers with high service records use the media and educational materials more effectively and significantly. In addition, female teachers act significantly more effective in expressing educational goals, selecting methods, media, teaching aids for training, encouraging students’ participations and evaluating and revising the teaching process.
 

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References

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History

  • Receive Date: 20 July 2017
  • Revise Date: 09 February 2019
  • Accept Date: 24 April 2018

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