Personal PD (cont.): Qualitative and Quantitative Methods for CALL Research

Leaky, J. (2011c). Chapter 5: A model for evaluating CALL part 2: Quantitative and qualitative measures. In J. Leaky. Evaluating Computer-assisted Language Learning: An integrated approach to Effectiveness Research in CALL (pp. 115-132). New York: Peter Lang.

Summary

In this chapter, Leaky (2011c) continued outlining his evaluation paradigm by examining how qualitative (judgmental) and quantitative (evaluative) approaches can be used to evaluate the efficacy of computer-assisted language learning (CALL) interventions. He provided nine principles to guide CALL research, which included controlling for confounding variables, doing a literature review, using random sampling, and providing a transparent methodology section (among others). He then provided a research design checklist to guide instrument design. This multipoint checklist covers matters of sampling (N size); integration of program, pedagogy, and platform; the conditions of the study (e.g., activity type, variables, etc.); and the quantitative instruments to be used. Qualitative methods and measures are absent from this methodological check list.

Response

In this chapter, Leaky continues his combative approach to building his own framework. This attack on the work that has come before not only sets a disappointing tone, it also introduces weakness into his arguments. He continues to conflate qualitative research with mere subjective storytelling by holding qualitative research to the same validity/reliability measures as quantitative research and presenting a 2D view of qualitative research. His tone throughout the chapter continues to alienate the reader, at least this reader. And, again the most useful elements of the section lie in poorly supported graphical elements. Namely, his checklist for designing CALL efficacy studies. So, if you want to skip the entitled dross, move to pages 125-126 and update the list to be actually mixed-method by considering how robust qualitative research can enrich the quantitative data—something Leaky (2011c) continues to fail to do.

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