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Measuring strategic processing: future directions

Earlier we discussed the different chapters in the section on measuring strategic processing and we paid attention to the relation between the conceptualization and the operationalization of strategic processing. During the discussion of these chapters we raised some salient issues related to the measurement of strategic processing. Most clear of these is the call for triangulation. Triangulation is used to refer to the use of multiple approaches to researching a question and is typically associated with research methods and designs (Haele & Forbes, 2013). The assumption raised at the start of this chapter that the field is beginning to move away from a reliance on self-reports and towards finer grained measures of strategic processing is therefore only partly true. Finer grained approaches are indeed increasingly important, but in the call for triangulation there is still an important place for self-report measures. In the remainder of this chapter, we would like to discuss other variations of triangulation that offer interesting opportunities and new challenges to use the different measurements of strategic processing in concert.

Triangulation of methods and data is probably the most well-known and utilized way to combine methods. While the opportunities are clear, the challenge in mixing methods and data is that different measurement levels and theoretical levels can get mixed and that, in the end, we do not know what we are talking about anymore. At the measurement level, Lonka, Olkinuora, and Makinen (2004) made a useful distinction between three levels of granularity in which inventories of strategic processing are used: general, course-specific, and situational. Alexander (1997) distinguishes between micro, mid, and macro levels of theories in which macro-level theories take a lifespan perspective to explain learning, micro-theories explain changes for a specific concept at a specific moment in time, and mid-level theories are situated in between. While the three levels of granularity suggested by Lonka et al. (2004) and the three levels of theories suggested by Alexander (1997) have been successfully used to foster clarity in research in which different types of self-report instruments have been mainly used to triangulate data, the different types of measures that were discussed in the chapters by Catrysse et al. or by Lawless and Riel would probably all be classified in the “situational” or task-specific level of measurement granularity and at the micro-level of theories. In order to foster both empirical and conceptual clarity in the research that uses and combines these types of measures with, e.g., task-specific self-report instruments, we would benefit from finer grained lenses and “nano-theories.” In order to stay meaningful for educational practices, these theories at the “nano-level” obviously need to be clearly connected to the theories at the micro-level. This is where the issue of theory-triangulation becomes relevant again. While triangulating different methods and measurement levels is more and more common, the triangulation of levels of theories often lags behind research. Sometimes we have to admit that our theories just fall short in explaining what is happening. In the case of Big Data this results in using a data-driven approach. The idea of triangulation could, however, also be used to combine data-driven approaches with theory-driven approaches. This could also allow triangulating multiple perspectives to students’ strategic processing, like student data, teacher data, parent data, peer-data, and Big Data. All these data-streams obviously offer too much information, but in the hands of multidisciplinary teams in which computer scientists and experts in the disciplines and educational psychologists work together, existing theories can be used to give meaning to and dig further into the data and then challenge, stretch, and reshape the existing theories again.

To conclude, we call for future research in which teams of experts in different disciplines work together to triangulate data and theory-driven approaches. Rather than focusing on the opportunities of the different “nano-level” measures of strategic processing and the advantages Big Data can give us to generate fancy but theory-less advice for the educational practice, such collaborations among teams of experts in different disciplines such as computer sciences and educational psychology have the potential to build better theories that can inform students and teachers. In this way, using all different measurement techniques in concert can truly be an added value for all.

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