Moving from Data Rich and Information Poor to Big Data, Big Gains
Over the past few years, k12 classrooms have been inundated with digital tools that make learning fun, and capture various data.
Teachers rejoiced at the variety of fun and engaging free digital tools that they could use with their students, the wonderful interaction the tools brought, and the great impact on classroom instruction that they had. They were able to see student responses and intervene promptly to correct misconceptions, to give students homework and track the progress through the tasks, they were even able to tag their test questions to learning standards to determine what understanding of the concept that the student had. Schools and district administrators rejoiced at the teacher enthusiasm and student excitement with the digital transition and looked forward to seeing improved learning outcomes.
Fast forward one year, and classrooms burst at the seams with a wealth of information about students. The teachers and administrators start realizing that some tools, while great at engagement, provide little else to help student learning, while others have information that they would like included with their regular, learning standards-based batch. So much data, so little time to put it all together, and such limited means to add it up and compare it! Some say it’s a square peg in a round hole, some call it apples and oranges, some say it’s too much, while others say it’s not enough. It seems we’ve struck gold, but don’t know how to dig it out! Welcome to the data rich and information poor era!
This is when we start realizing that we have access to an enormous and varied pool of information, but have little means to make it work together to have an accurate and aligned image of the student progress and truly personalize their learning.
We can call this the k12 Big Data Dilemma. As we well know, the Big Data is present in all areas of our life, caused by our adoption of digital tools. To make sense of the Big Data, companies have algorithms that mine the data and make connections. They then produce a report of the findings, with correlations and possible recommendations. In k12 we call that a really good teacher. However, with the amount and variety of data we now have, and adding to that the experience the teachers have aggregating so many data points, we soon see that we, too, need an algorithm, as well as a plan. The great awakening hence begins with a big question: how do we help our teachers use this Big Data? How can a teacher use a wonderful free or paid digital tool, but get relevant data that can be added to the mix to create a full and comprehensive view of the student’s performance?
How do we move from “Data Rich, Information Poor” to “Big Data, Big Gains”?
In a future not far away, we will all be able to replicate Alt School, a place where technology and good teaching come together to provide students with personalized education based on the data. But to get there tomorrow, we have to build realistic and systemic supports now. The first step is to ensure student digital access, and that all the online learning systems, which collect vast amounts of data as students progress through the game, test, or learning activity, are able to import/export data in standard formats that allow for the data to be aggregated accurately, and simplify the cross-walking work that districts and schools currently have to do to get relevancy and accuracy.
The second step in moving the district to big gains from big data is to build Digital Assessment Literacy skills in teachers and staff. With the new ESSA allowing district more freedom in the assessment selection and implementation of educator-controlled, local performance assessments, the requirement is also that the assessment system must be valid and reliable, aligned with the state academic standards, provide coherent and timely information about the student’s mastery level, and be provided in multiple languages for English Learners.
Because so much of the student academic data comes from teacher-student interaction in class, and this information is “live”, it is important to learn how to capture it in a meaningful format, which enables for it to be aggregated. It was therefore important to train teachers on how to build digital assessments that are reliable and standard aligned.
Schools are becoming accustomed to teaching and learning on mobile devices, and online formative assessments are increasing at a vertiginous rate. To support the digital transformation, and prepare for next gen assessment, Digital Assessment Literacy is focused both, on the utilization of assessments and data, and on the assessment authoring aspect. Data relevance, heat maps, and Data Driven Instruction (DDI) are elements that campus and district staff must continuously train on and putting in practice when creating assessments, as well as when making data driven learning decisions. Purchase a formative assessment platform to house all your assessments in one pace, and where data from other platforms can be aggregated. enables the test authors at all levels to align the test items to learning standards, and embed accessibility features, which mimic the state standardized assessment accessibility features, to support all student learning needs. It should also provide a reporting feature that is easy to read and helps teachers support students’ learning with personalized learning resources.
Maybe the most important and valued feature is the student facing data, which is released at the end of the test, where students see what questions they missed and how they are connected to their learning, before they even see the grade.
Big or small, campus or district, for anyone trying to move from Data Rich to Big Gains, here are three elements that must be present for successful transition to Data.
Digital Assessment Literacy – Maybe one of the most needed, assessment literacy helps teachers understand how to construct their tests for relevance, validity and maximum reach, without taxing students with long or frequent assessments. Guiding questions can be: what test to use when, how do we code test items, is one question relevant to determine student mastery?
Data Literacy - Without knowledge of data, teachers are unable to guide their students efficiently through their learning, Moreover, they would not understand how to build activities, assessments and projects that yield the much-needed “live” academic information. Guiding questions can be: what data is needed in my subject, should reading level matter in Math, how much weight should I give my test, is grade only a number, do I have apples and oranges, what next?
Logistics and flexibility – The greatest challenge in achieving big gains from big data is training teachers and administrators how to efficiently read, interpret and implement the data. Like all good PD, all training have to be applicable, in a low stakes environment, and with the focus being on mastering the Digital Assessment Literacy and Data Literacy processes. Guiding questions can be is PD in PJs helping teacher plan data-driven lessons, what tools do teachers use to gather data from student assessment, how to read a heatmap, and what are the next steps?
As we build upon Big Data, Big Gains, it is important to remember that our teachers are the ones who must understand and find easy to use the tools and processes that help our students be successful. The secret to personalization is in helping all stakeholders make sense of various data, which paint a complete picture of the student. The only way to do so without the great algorithms, is to build capacity in our teachers, so they create relevant data yielding lessons and assessments. And just like the Gold Rush, sharing out loud nuggets of data inspires others to equip themselves and go digging for it!