Educational Data Crossroads: Data Literacy for Data-Driven Decision Making in Postsecondary Education

Author(s):  
Soko S. Starobin ◽  
Sylvester Upah
2015 ◽  
Vol 117 (4) ◽  
pp. 1-50
Author(s):  
Ellen B. Mandinach ◽  
Jeremy M. Friedman ◽  
Edith S. Gummer

Background With the growing emphasis for educators to use data to inform their practice, little has been done to consider the means by which the educators can acquire the requisite data literacy skills. This article provides a context for why schools of education can and must play an important role in preparing teachers to use data. Purpose This article sought to understand if and how schools of education are preparing teacher candidates to use data effectively or responsibly. The study examined the extent to which schools of education teach stand-alone courses on data-driven decision making or integrate data use concepts into existing courses. It also examined state licensure and certification requirements to determine if and how data use is included in documentation. Population A stratified randomized sample of schools of education was drawn with 208 institutions responding, representing a 25.7% response rate. Research Design The survey portion of the study consisted of a stratified randomized sample of all schools or departments of education in the United States. The syllabus review was a voluntary part of the survey. The licensure review was a descriptive analysis of every state's documentation for teacher licensure and certification. Findings/Results The survey results indicated that a vast majority of the schools of education reported that they offered a stand-alone data course, and even more integrated data use into existing courses. The syllabus review provided a deeper dive into the course offerings and indicated that the courses were more about assessment literacy than data literacy. The licensure review yielded a plethora of skills and knowledge related to data that are included in state requirements. However, there was wide variation across states in their requirements. Conclusions Even though schools of education reported that they are teaching about data-driven decision making in their teacher preparation programs, the results indicate that the content is more about assessment literacy than data literacy. This finding is consistent with the often observed conflation of the two constructs. Licensure requirements include both data literacy and assessment literacy, but the emphasis is more on assessment than data. With the increasing emphasis by policy makers on the importance of educators using data, it is essential that schools of education begin to incorporate data concepts into their curricula and that states make explicit the data-related skills and knowledge required for teachers for licensure and certification.


2013 ◽  
Vol 42 (1) ◽  
pp. 30-37 ◽  
Author(s):  
Ellen B. Mandinach ◽  
Edith S. Gummer

Data-driven decision making has become increasingly important in education. Policymakers require educators to use data to inform practice. Although the policy emphasis is growing, what has not increased is attention to building human capacity around data use. Educators need to gain data literacy skills to inform practice. Although some professional development opportunities exist for current educators, fewer formal courses and opportunities for data literacy development in schools of education have been developed and implemented. This article explores issues around the growing need for data-driven decision making in programs in schools of education. The issues are complex and the actors needed to bring about change are multiple. A systems perspective to explore course and programmatic implementation is presented.


Author(s):  
Edmond Sebestyén

AbstractData-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.


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