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Data-driven decision-making (DDDM) has been playing an increasing role in contemporary teaching, since it includes systematic collection, analysis, and application of data to improve students’ educational performance. However, little is known about the affective factors that influence this data-based practice. Thus, the purpose of this study was to systematically examine previous research on the affective factors that influence DDDM based on the following criteria: (1) the level of DDDM usage; (2) the emphasis of affective factors; and (3) the nature of the interventions and their effects on teachers. According to the findings, this literature review showed how little DDDM-related affective factors have been researched, even though the knowledge of DDDM can help expand its application in the educational field. For example, although the most widely used tool is the  Data-Driven Decision-Making Efficacy and Anxiety Inventory  (3D-MEA), which has shown promising results in terms of measuring the efficacy and development of data literacy, other affective components have yet to be tested, due to their novelty in the field. The implication of the findings is that obtaining more information about DDDM and its affective elements can help reduce teachers’ anxiety toward this approach and ultimately enhance the overall educational process.

A literature review on the affective factors that influence data-driven decision-making