Managing and using DATA
Classroom Data: a Deep and Complex Subject By Harvey Hughes Data in the classroom focused on student achievement is a very complex issue for education making it impossible to discuss all the issues, usage and purposes in one discussion. A primary issue is that data and its purpose has been a moving target, as education reform movements have changed from a model focused on compliance to a model focused on accountability. In the past, student achievement data consisted mainly of a collection of historic data points such as grades, periodic progress notes and summary of assessment results. To some degree, the best educators could do was to teach the grade level lesson plan to the entire class and intervene when an exception was noticed. While this method was the best we could expect, now times have changed. We have better technology, better analytics and a better model. The model of accountability says we should focus on individual student outcomes more than the processes, to identify categories of struggling learners. The challenge with the accountability model is that we have to collect progress artifacts of data, publish the results, analyze the data, compare against expectations or norms, and then act upon the data analytics by prescribing educational interventions focused on best practices. Additionally, the challenge of performing this process for individual learners, not just categories of struggling learners, has added another layer of complexity. Ultimate Purpose of Smart Data Collected in the Classroom: Early identification of struggling learners with immediate and targeted intervention is the ultimate purpose and challenge of the accountability model of individual student achievement of education. Educators must continuously assess and monitor student progress against their Grade Level Expectations (GLE) as part of their classroom instruction. Although this seems like a difficult challenge it is a primary part of the accountability model to ensure that teachers know on a daily basis where their students compare to the state or common standards of GLE, thus giving them
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Spring 2015
SouthEast Education Network
some indication of student mastery or struggle. This early and continuous monitoring and measuring data is critical to aligning instruction to student achievement. Continuously measuring student performance toward their grade level expectation is the only way we will really know who is proficient and who is struggling. Unfortunately this is a very daunting task and virtually impossible without the help of proper technology in the classroom, support from the building administration and from the district wide culture to encourage relevant data collection at the classroom level. Challenge: The challenge of implementing strategies, technology and methodologies such as universal screeners for early identification of struggling learners, individual student assessments to uniquely understand missing skills or learning disabilities, building individualized learning plans with measurable goals and objectives, implementing strategies and interventions aligned to these achievement goals and objectives, then finally monitoring and measuring progress with analytic metrics to help recommendations toward a improvement plan is daunting to say the least, but research and common sense agree that we must monitor student achievement on a daily basis. Some would argue that we are overloaded with data, while some of us will argue that we need smarter data. I call this “Smart Data” — that is looking at signs of struggling learners, called hard and soft indicators. Smart data begs the question of “what indicators are hard indications of struggling learners such as attendance, discipline and course grades?” and “what soft indicators such as social/emotional signs and level of classroom engagement and how do they relate to the overall achievement level, and how can they be presented to the teacher in a quick and meaningful way for immediate strategy or intervention aligned to the daily lesson plan?” Smart data simply means collecting and presenting early warning signs of non-mastery and presenting them to the teacher in a way that dovetails with the lesson plan so all of this is happening seamlessly with the lesson pacing