在伊朗英语高等教育背景下进行质性调查的挑战:博士生和教师的声音

Hossein Bakhshi, H. Weisi, N. Yousofi
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引用次数: 4

摘要

本研究旨在探讨在伊朗高等教育背景下,从伊朗英语教学博士生和英语教学教师的角度进行定性研究(QLR)的挑战。研究对象为100名英语专业的博士生和50名英语专业的教师。本研究采用探索性设计的混合方法。用于收集数据的工具是半结构化访谈和研究人员制作的问卷。为了分析定性数据,我们采用了扎根理论的方法(Charmaz, 2006),为了分析定量数据,我们使用了描述性和推断性统计。研究结果表明,从英语教学博士生的角度来看,伊朗的教育制度、教授和缺乏时间分别是影响进行QLR可能存在的弱点的因素。而从英语教学教师的角度来看,缺乏时间是进行QLR的唯一原因。结果还表明,从英语教学博士生的角度来看,数据解释、数据分析、数据收集、数据编码和确定研究的有效性分别是进行QLR最具挑战性的部分。此外,从英语教学人员的角度来看,数据分析、数据收集、数据解释和数据编码分别是进行QLR最具挑战性的部分。讨论了本研究对调查背景下的大学的意义。最后,对进一步研究提出了建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Challenges of Conducting Qualitative Inquiry in the Iranian ELT Higher Education Context: Ph.D. Students’ and Faculty Members’ Voices
This study aims at investigating the challenges of conducting qualitative research (QLR) from Iranian ELT (English Language Teaching) Ph.D. students’ and ELT faculty members’ viewpoints in the Iranian higher education context. The participants of the study consisted of 100 Ph.D. students majoring in ELT and 50 ELT faculty members. The study followed a mixed-methods approach with the exploratory design. The instruments utilized for collecting the data were a semi-structured interview and a researcher-made questionnaire. To analyze the qualitative data, a grounded theory approach was employed (Charmaz, 2006), and to analyze quantitative data, both descriptive and inferential statistics were utilized. The results of the study indicated that from the ELT Ph.D. students' perspectives, the Iranian educational system, professors, and lack of time, respectively, are factors affecting the possible weaknesses in undertaking QLR. While from the ELT faculty members' points of view, lack of time is the only reason for weaknesses in undertaking QLR. The results also demonstrated that from the viewpoint of ELT Ph.D. students, data interpretation, data analysis, data collection, data coding, and determining the validity of research, respectively, are the most challenging parts of conducting QLR. Moreover, from the perspective of ELT faculty members, data analysis, data collection, data interpretation, and data coding, respectively, are the most challenging parts of undertaking QLR. The implications of the present study for the universities in the context under investigation are discussed. At the end of the paper, some suggestions for further research are presented.
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