Do schools of education have the capacity to give the next generation of educators the data literacy skills they need? As it stands now, the answer is no. Moreover, the vast majority of schools aren’t on the fast track (or any track) to building that capacity.
The education data literacy gap: The deans’ perspective
Over the past four years of research into education data literacy, I’ve spoken with dozens of deans about their schools’ capacity. Two exchanges are particularly telling. One dean noted that his institution needed to address the issue, but that he had no faculty who were capable of teaching a stand-alone data course. This same dean also noted that he lacked the wiggle room to add another requirement. The curriculum coverage required by the state was so full that he actually couldn’t add to it.
A second dean noted that he didn’t really see the need for faculty with expertise in data-driven instruction. If he had several open faculty slots, he said, knowledge of education data and its classroom application would be low priority in terms of skills he hired for. Instead, he’d prioritize expertise in traditional topics such as mathematics education and instruction.
These examples provide a snapshot of how most schools of education currently approach the issue: Existing constraints and a lack of urgency ensure that skills in data-driven decision making remain a second-tier priority. Yet, the reverse should be true.
The need: Tools will fail teachers until they have the training to put insights to work
Data, broadly and richly defined, can and should be applied at all levels of the education system to address pressing educational questions, issues and problems. Data literacy is relevant across all grades, content and levels of the education system. Data provide educators and students with the concrete evidence they need to improve their learning. Meanwhile, new tools give educators the ability to extract insights from data, but until teachers have the skills to apply such insights in class, tools will miss the mark. Teachers will feel overburdened by the influx of information; students won’t receive the expected benefits; and the whole enterprise of teaching and learning will remain stuck in place.
Data provide educators and students with the concrete evidence they need to improve their learning.
Real constraints: Terminology, expertise, urgency
That said, the constraints faced by schools of education are real. A quick run-down:
- We don’t yet have a sectorwide definition of education data literacy. Last year, WestEd convened dozens of experts to arrive at a consensus definition of data literacy, but in general usage, there’s still ample confusion to go around.1
- We don’t have the higher ed expertise to teach data literacy. Only a handful of professors specialize in data-driven decision making nationwide. They are slowly producing mentees who can train aspiring educators with the skills they need, but it will be a long time until we see a critical mass of such experts.
- Motivation for schools of education to provide teaching candidates with data literacy skills is lacking. Educators in the field face increasing pressure to use data to make decisions, but there are few licensure requirements or other mechanisms to ensure that schools feel similarly pressured to train educators with those skills.
Promising opportunities: Ancillary materials, virtual courses and MOOCs
How can we overcome these gaps? Three modest proposals spring to mind:
- Experts with extensive instructional and data literacy content knowledge could develop a portfolio of materials and resources that professors could integrate into existing courses. This would enable the integration of ancillary materials into existing suites of courses such as measurement, statistics, educational psychology, instruction, methods and pedagogy. The obvious hurdle is that professors responsible for integrating the materials would need to understand the materials (which might require training) and prioritize integration.
- Schools could offer limited enrollment virtual courses conducted either by professors at other institutions or by professional development providers who specialize in data-driven decision making. Such courses could be introduced across the developmental continuum, targeting beginning to experienced educators, and tailored for undergraduate to graduate students.
- A reputable institution could create a MOOC-style (massive open online course) or series of MOOC-style courses focused on data-driven decision making. These courses would include videos, readings and problem sets, as well as interactive user forums where students, qualified professors and teaching assistants could interact. They could be designed for undergraduate and graduate levels. If schools of education were willing to require participation, the challenge of completion (which has typically dogged MOOC-style offerings) would be a nonstarter.
All three options have costs associated with them, but each begins to address the challenges currently faced by schools of education. The question I’d like to raise here is: What other options might be available? We would like to hear from you.
Ellen Mandinach is a leading expert in data-driven decision making, focusing on understanding how educators are using data to inform practice. She serves as Senior Research Scientist and Director of the Data for Decisions Initiative at WestEd.
Read Ellen’s related posts, Data literacy vs. assessment literacy and Licensure as a lever to drive education data literacy.
1 The consensus definition is: Pedagogical data literacy or data literacy for teaching is the ability to transform information into actionable instructional knowledge and practices by collecting, analyzing, and interpreting all types of data (assessment, school climate, behavioral, snapshot, etc.) to help determine instructional steps. It combines an understanding of data with standards, disciplinary knowledge and practices, curricular knowledge, pedagogical content knowledge, and an understanding of how children learn.