Are You Ready to Use Technology in EFL Teaching? Examining Psychometric Properties of EFL Teachers’ Technological Pedagogical Content Knowledge (TPACK) Scale

Liwei Hsu

Abstract


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.


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DOI: https://doi.org/10.5296/ire.v4i1.8740

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