Bodong Chen

Crisscross Landscapes

Notes: Mandinach. (2016). Teachers learning how to use data: A synthesis of the issues and what is known



Citekey: @Mandinach2016a

Mandinach, E. B., & Jimerson, J. B. (2016). Teachers learning how to use data: A synthesis of the issues and what is known. Teaching and Teacher Education, 1–6.


This is an editorial of a very timely issue on data literacy of teachers, to be published in Teaching and Teacher Education. This editorial highlights several crosscutting concepts in this area: (a) the need for continuous learning around data e the where and when; (b) the need to integrate data skills with content knowledge and pedagogical content knowledge and link the data skills to other aspects of effective teaching; © the sustainability of impact; (d) models of effective professional learning and the impact of collaborative inquiry and data teaming; and (e) the logic model on impact of data use.

In addition to these aspects, what is of special interest to me is that Jimerson et al (this issue) take “the need for data literacy a step beyond teachers to include students themselves as primary data users”, which corresponds to my attempts to give students access to social network data in an online class. Another quote:

One of the five essential recommendations posed by Hamilton et al. (2009), based on an extensive literature review, was for students to become their own data-driven decision makers.

Overall, this issue is an important, timely addition to the literature and would be of interest to many parties (e.g., teacher ed programs, academic tech offices at universities, Unizin) and research communities (e.g., learning analytics).


building blocks for data use must be laid in teacher preparation programs (Mandinach & Gummer, 2016; Reeves & Honig, 2015), (p. 1)

Using data in education is not new; it has been around for a long time. However, the emphases for education to become evidence-based have been increasing in many countries. (p. 1)

Leaving the acquisition of data literacy to traditional forms of professional development is risky at best. (p. 1)

Despite the emphases and the recognition that teachers must be equipped with data, little comprehensive effort has been made to improve the capacity of educators to use data effectively and responsibly through teacher preparation programs and explicit requirements for licensure. (p. 1)

Funding is scarce for professional development and many schools may consider data use to be peripheral, opting instead to focus on topics that are considered more pressing. (p. 1)

For data use to be woven into day-to-day practice, rather than being perceived as an “add-on” to the hard work teachers already do, continuous learning must occur throughout one’s career (p. 1)

  1. Cross-cutting topics: what’s important for learning about data (p. 1)

Hoogland et al. (this issue) note the importance of data literacy and beliefs in data use. Mandinach and Gummer (this issue) outline a construct, data literacy for teachers, that contains some 53 skills and sources of knowledge teachers need to use data effectively and responsibly. (p. 2)

Topics include: (a) the need for continuous learning around data e the where and when; (b) the need to integrate data skills with content knowledge and pedagogical content knowledge and link the data skills to other aspects of effective teaching; © the sustainability of impact; (d) models of effective professional learning and the impact of collaborative inquiry and data teaming; and (e) the logic model on impact of data use. (p. 2)

Fundamental to the conceptual framework is that data skills for teaching must be surrounded by several forms of knowledge, most specifically content knowledge and pedagogical content knowledge (Gummer & Mandinach, 2015). (p. 2)

That Farrell and Marsh (this issue) as well as Van Gasse, Vanlommel, Vanhoof, and Van Petegem (this issue) find few instances of transformed instruction as a result of data use suggests that a gap exists in how the field supports teachers to develop just this capacity. (p. 2)

1.1. Continuous learning (p. 2)

Most commercial professional development providers of data use admit that they do not go far enough to connect to the pedagogy (Mandinach & Gummer, 2013) (p. 2)

Mandinach and Gummer (2013, 2016) lay out the rationale for why schools of education must step up to begin to address the lack of human capacity around data skills. (p. 2)

Preparation programs must introduce data use to teacher candidates, who must then have data use-related knowledge, skills, and dispositions reinforced through in-service training and professional development throughout their respective careers. (p. 2)

A question we must raise is what is the base level of each type of knowledge (e.g., data skills, content knowledge, and pedagogical content knowledge) required to serve as a starting place for effective data use? The field needs to explore the relationships among these skill sets. (p. 2)

every educator must have some passing knowledge of inquiry, data collection, data interpretation, and application to instructional planning and delivery. (p. 2)

Jimerson, Cho, and Wayman (this issue) for example, highlight the ways in which in-service teachers may struggle to fit data to practice when data-related concepts have not been well-introduced in teacher preparation programs. (p. 2)

the absence of solid mental models for data use (p. 2)

As Means, Chen, DeBarger, and Padilla (2011) found, teachers with good data skills who work in collaborative teams can compensate and backfill for other teachers who lack such skills. But as van Gasse et al (this issue) note, collaboration does not occur automatically, having found little collaboration among teachers on data use. (p. 2)

Teacher beliefs about data use matter, and so learning around data must address not only the technical aspects of data use (e.g., accessing systems or interpreting reports) but also the assumptions teachers make about what data “count” and whether or how data use benefits students. (p. 2)

1.2. Data skills: an integrated approach (p. 2)

1.3. What is effective teaching and how are we measuring it? (p. 3)

One of the five essential recommendations posed by Hamilton et al. (2009), based on an extensive literature review, was for students to become their own data-driven decision makers. But for students to be data literate, teachers first need to have the necessary skills, knowledge, and dispositions that are part of data literacy. They must then be able to facilitate the development of these skills and dispositions among their students in appropriate ways; this is a lofty goal, and one not without challenges. (p. 3)

Van den Hurk, Houtveen, and Van de Grift (this issue) discuss effective teaching. What is interesting about this article is that it outlines an instrument that contains 24 items related to effective teaching, yet not one item explicitly pertains to data use. This reflects a hole in the current literature where there still is no recognition of the importance of data use in teachers’ practice, or where exists only a surface recognition of data use as merely tangential to other components of practice (e.g., feedback, assessment, or planning for remediation). (p. 3)

1.4. Sustainability of impact (p. 3)

Data use must become an integral and integrated component of educators’ work, just like any other method. (p. 3)

Educators must recognize the utility of data use and its possible impact. But the data must be useful, informative, and actionable. (p. 3)

The Poortman and Schildkamp (this issue) article raises the issue of sustainability by noting the need for continued support. Jimerson et al (this issue) point out that even “true believers” in student-involved data use note frustrations and challenges that, unless addressed, may dissuade others from even trying to adopt such practices; (p. 3)

Lai and McNaughton (this issue) have outlined a three-year model that tests the limits of feasibility. How many school districts can afford three years of professional development in terms of practical and financial constraints? How many school districts have the same educators after three years, given high rates of mobility? (p. 3)

Together, several of these articles provide different peeks into how data are woven into professional learning experiencesdimplicitly or explicitly. (p. 3)

The Van den Hurk et al (this issue) piece provides an in-depth look at a process in which teachers use structured and reflective data on a number of teaching behaviors visa-vis video observations and feedback loops. (p. 3)

Means, Padilla, and Gallagher (2010) found the importance of sustained support around data use. (p. 3)

Also of importance here is a finding that Mandinach and Friedman (2015) noted in case studies of exemplary data-oriented teacher preparation programs. (p. 3)

The Poortman and Schildkamp (this issue) piece provides another interesting twist by employing a methodology that allows for effectiveness to be measured specific to the problems-to-besolved among data teams. In the model described by this article, teachers not only use data to explore and address problems of local import, but learn about data use in context as they do so. (p. 3)

Farrell and Marsh (this issue) raise a similar issue about embedding data use in a strong school culture (p. 3)

The Farrell and Marsh (this issue) piece also suggests that classroom change happensdmost likelydas collaborative groups of teachers work toward instructional improvements, supported by external and internal data; again, we see a different take on teacher learning that still highlights a learning-by-doing orientation when it comes to using data. (p. 3)

1.5. Models of effective professional learning (p. 3)

The Jimerson et al (this issue) article takes the need for data literacy a step beyond teachers to include students themselves as primary data users; (p. 3)

A common thread throughout the pieces is that, by building capacity for data use, teachers can be empowered to drive their own learning in ways that are sensitive to the specific needs of the local contextdthey need not always wait on a school or system leader to determine professional development needs. (p. 4)

Van den Hurk et al (this issue) cite work by Wayman and Jimerson (2014) on the characteristics needed for effective professional learning. They include: collaboration, engagement, context, embedding in the job circumstances, intensity, and coherence. (p. 4)

1.7. Logic model on impact of data use (p. 4)

Underlying data use is the expectation that data-driven decision making will improve student performance. The logic model is multi-part: (a) train teachers to use data; (b) there will be an impact on classroom practice; and © those practices will lead to improved student performance. Poortman and Schildkamp (this issue) and Lai and McNaughton (this issue) mention part of this logic model and raise questions about it, but perhaps we should all question the seductive nature of such a simple logic model for how data use works. (p. 4)

Berliner (2006), in an article based on a rousing keynote address at AERA in 2005, noted that there are many things beyond the control of teachers, such as childhood health issues, poverty, hunger, and lack of parental support. (p. 4)

1.6. Collaborative inquiry and data teaming (p. 4)

It is therefore of little surprise that the rigorous studies (Carlson, Borman, & Robinson, 2011; Konstantopoulos, Miller, & van der Ploeg, 2013) found mixed results on the impact of data use on student achievement. (p. 4)

In order for data teaming to function effectively, there must be certain structures and process in place. School leadership must provide the needed resources and time for teams to meet. Leadership must create an open, trusting, and nonevaluative environment in which team members can discuss issues and feel free to dissent or even admit their challenges. (p. 4)

Still, what we should not dissuade us from doing what we can do to support student learning, supported by data. We would therefore suggest talking about the logic model and focusing on the linkages between preparation on data use or using data on what happens in the classroom. (p. 4)

  1. Concluding thoughts (p. 4)

Yet collaboration looks different in each of the articles. Van Gasse et al (this issue) use collaborative exercises around student learning outcomes. (p. 4)

Lai and McNaughton (this issue) focus on building leaders for data teaming (p. 4)

Across contexts, we see data use not as a clear, linear process of neatly sorting students into tutoring groups by areas of need, but as a messy, iterative process that requires critical thinking skills, innovation, a dogged determination to inspect ourselves and our contexts, and to play the role of educational detectives to seek out root contributors to student (and system) underperformance. (p. 5)

the field must consider data more broadly, beyond assessment data. (p. 5)

Third, with increasing problems with data security in many venues, it is essential for educators to understand how to use data responsibly. (p. 5)

We also see a concern for appropriate use of data by teachersda recognition that data use is not an inherent good, but that it must be implemented in ways that are ethical and which treat data use as a means to an end, rather than an end in and of itself. (p. 5)

In this collection of papers we see increased attention devoted to examining how data informs not only teaching, but how it informs teachers themselves in terms of reflective practice. (p. 5)

And yet, if we are serious about data use being woven into practice, and being used in conjunction with pedagogical content knowledge as well as content knowledge, we must challenge the field to push beyond studies that use “data use” as a keyword to see whether and how elements of data use appear in studies in the content areas. Similarly, what relevant literature might we miss because the research appears in a publication that may not be easily available in other countries? (p. 5)

Because of the distinction between data literacy and assessment literacy (Mandinach & Gummer, 2016; also this issue), the field must look beyond student achievement and test results as a primary indicator of impact of data use. Thus, the field also needs more work that measures diverse outcomes of data use in action; this meaning including not just typical achievement-oriented student outcomes, but also affective, conative, behavioral, and other metrics. (p. 5)

To make the necessary progress, several key challenges must be overcome. One challenge is to ensure that all educators know how to use data effectively and responsibly. This barrier requires action by educator preparation programs, professional development providers, school systems, and other relevant stakeholders. This means that educators must be exposed to data literacy as early as possible and then have data literacy reinforced throughout their careers (Mandinach & Gummer, 2016; also this issue). (p. 5)

Berliner, D. C. (2006). Our impoverished view of educational reform. Teachers College Record, 108(6), 949e995. (p. 5)

Second, there still needs to be a common and accepted language of what it means for educators to be data literate. This has been the goal of the work of Mandinach and Gummer and the Data Quality Campaign (2014) in positing definitions. (p. 5)

Gummer, E. S., & Mandinach, E. B. (2015). Building a conceptual framework for data literacy. Teachers College Record, 117(4). Retrieved from PrintContent.asp?ContentID1⁄417856. (p. 5)

Even Reeves and Honig (2015) who explored data literacy in pre-service teachers, focused solely on assessment data, and failed to take a broader view of what constitutes data literacy. (p. 5)

Jimerson, J. B., Cho, V., & Wayman, J. C.. (this issue). Student-involved data use: Teacher practices and considerations for professional earning. Teaching and Teacher Education, 60c. (p. 6)

Reeves, T. D., & Honig, S. L. (2015). A classroom data literacy intervention for preservice teachers. Teaching and Teacher Education, 50, 90e101. (p. 6)

Shulman, L. S. (1986). Those who understand: Knowledge growth in teaching. Educational Researcher, 15(2), 4e14. (p. 6)

Mandinach, E. B., & Gummer, E. S. (2013). A systemic view of implementing data literacy into educator preparation. Educational Researcher, 42(1), 30e37. (p. 6)

Mandinach, E. B., & Gummer, E. S. (2016). Data literacy for teachers: Making it count in teacher preparation and practice. New York, NY: Teachers College Press. (p. 6)

Means, B., Chen, E., DeBarger, A., & Padilla, C. (2011). Teachers’ ability to use data to inform instruction: Challenges and supports. Washington, DC: U.S. Department of Education, Office of Planning, Evaluation, and Policy Development. (p. 6)

Wayman, J. C., & Jimerson, J. B. (2014). Teacher needs for data-related professional learning. Studies in Educational Evaluation, 42, 25e34. (p. 6)