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    The Power of Data in Blended Classrooms

    One of the most integral elements to implementing personalized blended learning is the ability to use data to inform daily classroom instruction. Data allows teachers to unlock the full potential of student-centered learning; without accurate and actionable data, teachers are unable to deeply personalize instruction because they may lack immediate and comprehensive knowledge about each student.

    In all of the Raising Blended Learners (RBL) sites, understanding the value of data in a personalized blended learning setting has been pivotal to early successes. However, the way most schools use data today is not as developed as the data use required by personalized blended learning. Across all sites in year 1, RBL has revealed that either: 1) data practices are not robust enough to fully implement personalized blended learning and/or 2) even strong data practices require enhanced skills in a personalized blended environment. As a result, sites have had to fundamentally shift their mindset and practice around what both data is and how to best use it.

    Understanding why current data practice falls short

    In our accountability-driven education system, ask any teacher, school leader, or district administrator, and they would tell you that data is important. However, because blended personalized learning requires using data in new and different ways, its effective use in a student-centered classroom necessitates a complete shift in mindset, meaning a redefinition of what we think and believe about something to motivate a behavior shift. The mindset shift around data occurs when data is seen as more than simply an accountability measure but rather a way to better inform instruction, and the teacher is motivated (believes he or she is capable of) to use that data better to inform teaching and learning.

    WestEd has articulated this dissonance of seeing data as accountability only and using data as an instructional tool as the difference between “assessment literacy” and “data literacy,” arguing that “conflating the two terms implies that the only data teachers should use are from assessments.” As WestEd explains, “assessment literacy” is the understanding and use of end-of-year standardized test performance data. Looking at assessment data is common in schools and is therefore often mistaken as the best – or only – way to analyze data. In contrast, the term “data literacy” goes much further to incorporate the use of multiple forms of data sources and requires teachers knowing how to link data to daily instruction. WestEd formally defines data literacy as:

      “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.”

    Standardized test data, therefore, may be integral to understanding student performance, but it is only one piece of the data teachers can collect and use to inform instruction. To move beyond assessment literacy toward true data literacy, educators should incorporate multiple forms of data (to include standardized test, growth data, daily exams, behavior observations, student perception and more) and possess the skills needed to link data to daily instruction.

    Data practices in Raising Blended Learners sites

    This gap between assessment literacy and data literacy was exhibited across all Raising Blended Learners sites in year 1. In RBL sites, assessment data is provided through the State of Texas Assessments of Academic Readiness (STAAR) end of year standardized test. As the measure for statewide accountability, the STAAR exam provides valuable information on how students perform on-grade-level at the end of the school year. However, because personalized blended learning creates a need for more frequent (daily/weekly/monthly) and more detailed (beyond grade level/proficiency based) data sources, the STAAR exam is unable to inform instruction at a classroom level. The STAAR exam is not a classroom instruction tool, nor was it intended to be, but given its importance on a state level and resulting prevalence in schools, it is still used frequently for this purpose.

    As RBL teachers began to personalize learning in year 1, they discovered the gap between the assessment data they were using from STAAR and the evolving needs of their blended classrooms. This understanding created a need for districts to develop a data culture that includes more than assessment in which all individuals — from students to superintendents — embrace multiple data sources to measure student success and align daily instruction.

    In Birdville ISD, when teachers began using Star360 reading screeners for the first time in year 1 of their pilot, they began to see precisely why having multiple forms of more frequent data was necessary. As one teacher explains, having more frequent data “helps me when I’m grouping kids…because I have to change my lessons up and change my approaches in all of my classes – I can’t teach every class the same.”

    Where sites were able to shift mindsets around what data could be collected and how often it was used, they were able to find success with their pilots that pushed their implementations further, faster. In many cases, change in belief around data flipped the switch in blended classrooms and gave teachers increased confidence in their pilots.

    In Cisco ISD when teachers began using NWEA MAP formative growth data multiple times throughout the year, they realized that knowing students passed the STAAR was “not enough.” As the Cisco RBL Project Manager explains, after the second administration of MAP, pilot teachers were able to see comparative analytic growth data and target specific Texas Essential Knowledge and Skills (TEKS) standards for individual students, dramatically improving their ability to personalize instruction and meet students exactly where they are. While the NWEA MAP is an assessment, and could be part of an “assessment literacy” culture, we believe the use of NWEA MAP is clarifying around holes in proficiency and numbers of years behind that a student is in primary academic subjects. Knowledge of a student’s historical academic progress and mindset about their academic abilities is essential in a strong culture of data literacy, instead of assessment literacy.

    In year 1, RBL sites made considerable progress toward changing their mindsets around the multiple forms of data necessary and how to use for instructional purposes. Specifically, a survey FSG conducted of RBL teachers at the end of the first year of implementation showed that pilot teachers regularly use many forms of data to plan classes and tailor what they teach to students. These multiple data sources include: STAAR data, software data (e.g., DreamBox, Summit Learning), student self-assessments, unit or benchmark assessments, NWEA MAP data, exit tickets, Star360 data, student videos, daily assignments, mentor check-ins, and content assessments.

    Source: FSG Raising Blended Learners Teacher Survey, Spring 2017.

    The volume of data reportedly used by these pilot teachers in year 1 demonstrates a mindset shift beyond assessment data; however, the work is far from complete. As sites head into year 2, their behavior and practice using these new data sources must also evolve. As the Project Manager in Point Isabel explains, once pilot teachers and leaders began to see more granular data they immediately understood its value – but shifting daily practice will require training, support, and persistence as pilot progress.

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