scholarly journals The manifestations of micro and macro categories of pedagogical content knowledge in the practices of prospective EFL teachers

2020 ◽  
Vol 16 (2) ◽  
pp. 661-683
Author(s):  
Arif SARIÇOBAN ◽  
Özkan KIRMIZI
2021 ◽  
Author(s):  
Rana Najjari ◽  
Gholam reza Abbasian ◽  
Massood Yazdanimoghaddam

Abstract This research aimed to study the impact of “Technological Pedagogical Content Knowledge” (TPACK) workshops on Iranian EFL teachers’ and their TPACK progress after implementation of workshops. TPACK workshops were implemented by using TPACK framework (Koehler & Mishra, 2006) and “learning by doing” method (Hegelheimer & Chapelle, 2004) to investigate in what manner English teachers expand TPACK skills and use it in instruction. 15 EFL teachers who attended this qausi-experimental research design were the participants of this study who attempted two separate TPACK literacy, implementation and perceptions questionnaires. Findings resulted in positive contribution to the target teachers after TPACK workshop implementation. Moreover, it was observed that participants’ perceptions towards TPACK literacy developed in the light of TPACK workshops. Thereby, the study develops a new perspective and provides empirical evidence in the investigation of integrating technology and knowledge in teaching English and Computer-Assisted Language Learning (CALL) in Iran. Also, the theoretical and pedagogical recommendations for future research and practice are provided.


2015 ◽  
Vol 8 (11) ◽  
pp. 155
Author(s):  
Wei Xu

<p>Any instructional practice must be derived from a teacher’s knowledge base for teaching, which can be acquired by training, study, or practice. While much attention has been paid to teachers’ practical content knowledge in real educational settings, comprehensive syntheses of expert knowledge on a particular teaching task for a specific group of teachers are still scarce. This paper tends to synthesize ESL/EFL teachers’ pedagogical content knowledge of reading strategy instruction through learning the expertise conveyed in literature. Drawing on related studies in the field of reading strategy instruction either in general or in ESL/EFL contexts, this argumentative article first proposes a synthesized reading strategy instruction model which consists of one key component and two general principles, all of which create and are created by a safe and risk-free environment where students learn to use strategies actively and consciously with motivation and assistance. This article then elaborates on eight instructional strategies using summarizing instruction as an example in terms of three types of knowledge: declarative, procedural, and conditional. With the enrichment of the pedagogical content knowledge on strategy instruction, ESL/EFL teachers might teach reading strategies effectively both <em>with</em> metacognition, i.e., consciously planning, monitoring, and evaluating their teaching, and <em>for</em> metacognition, namely, to affect their students’ metacognitive awareness of strategy use in reading.</p>


Author(s):  
Mehrak Rahimi ◽  
Shakiba Pourshahbaz

Teaching is known to be one of the most stressful occupations ever since teachers are believed to suffer from different social and political discriminations leading many of them to feel frustrated. In the 21st century, the force of technological empowerment and ICT integration in schools has multiplied the stressful nature of the profession for teachers. To make the whole process of education more successful and to protect the wellbeing of teachers, empowering teachers to handle their job appropriately is a very crucial issue. TPACK (technological pedagogical content knowledge) is the knowledge of most value in today's world, and understanding this complex knowledge is the very first step on the path of successful ICT integration into the process of teaching. With that being said, this chapter aims at investigating the concept of TPACK in terms of education in general and language teaching in particular.


2017 ◽  
Vol 101 (1) ◽  
pp. 109-127 ◽  
Author(s):  
Johannes König ◽  
Sarantis Tachtsoglou ◽  
Sandra Lammerding ◽  
Sarah Strauß ◽  
Günter Nold ◽  
...  

2016 ◽  
Vol 4 (1) ◽  
pp. 97
Author(s):  
Liwei Hsu

<p>It has been confirmed that technology can be beneficial for students’ academic performance, including in the field of computer-assisted language learning (CALL). The successful administration of CALL depends greatly on the teachers’ knowledge about technology, pedagogy and content. The aim of this study is to explore the psychometric property of measure of EFL teachers’ technological, Pedagogical and content knowledge (TPACK). One hundred and fifty-eight EFL teachers were invited to join this study through stratified randomization sampling technique. The research instrument was the TPACK-EFL and the exploratory factor analysis (EFA) with extraction method of Maximum Likelihood and the rotation method of Promax with Kaiser Normalization, was performed to extracted factors with factor loading above .50. Seven constructs (Technological Knowledge, Pedagogical Knowledge, Content Knowledge, Technological Pedagogical Knowledge, Technological Content Knowledge, Pedagogical Content Knowledge and Technological Pedagogical Content Knowledge) were retrieved. Afterwards, the Confirmatory Factor Analysis (CFA) was undertaken to examine the convergent and discriminant validity of selected factors. Convergent validity was checked with Composite Reliability (CR), Average Variance Extracted (AVE), Maximum Shared Variance (MSV), and Average Shared Variance (ASV). Suggested value for CR and AVE was .6 and .5 respectively while MSV as well as ASV should be lower than AVE. Results showed that constructs of this study all met the requirement which indicated that the items had convergent validity. In terms of discriminant validity, square root of AVE was greater than inter-construct correlations which asserted the discriminant validity of this instrument. Subsequently, alternate model analysis was conducted to yield the model which fitted the best as indicated by the model fit indices and research context.</p>


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