Finding Value in Student Data
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Educators produce and consume a lot of data. However, they often do not have insights into how, and why, personal and performance data on students is collected. While appropriate understanding of how data is collected and processed may not be necessary for directing and improving instruction, an understanding of how data elements correlate with each other certainly is.
When one considers how educators have traditionally used student data for decision making, one may envision a spreadsheet or one-page visualization – searching for individual data points that provide some degrees of insight into why a student is performing at a certain level. Of course this does not provide anything more than a cross section of a single student. It is difficult to identify trends that may be general to all students, or specific to certain subset of students. For example, we may know intuitively that students in poverty perform at lower levels than students from median to upper income households. Knowing this does not provide a great of information, though, on how these students may be better assisted or provide any form of warning system for when students may be at-risk of falling behind in general.
Data visualization, particularly visualization that allows the end-user to scale their own questions provides much better leverage. Imagine a scenario where a teacher could look at her class holistically and then ask questions about trends in their students’ performance and then drill down into those trends to identify common elements of that trend – all with one click. For example, a teacher may understand that students on free/reduced lunch perform lower than other students but may drill into that to determine that when tests are given on Mondays those students perform worse. Why might this be the case? Generalizing for a moment, it may be that these students physiological needs were not fully met over the weekend and so they are underprepared both physiologically and psychologically for an assessment on a Monday. A ha, a point we can leverage to (1) potentially bring their grades up, and (2) potentially identify programs to address those physiological needs.
At a district level, imagine a scenario where an analyst could use historical data to model performance to predict the probability of individual students reading at grade level. While this is a task that many analysts can accomplish on their own, providing those results to a Superintendent or Instructional Coordinator may be difficult. What if they could easily visualize their analysis in an easily consumable and interactive way. District leadership now has the ability to assess future performance at scale and plan interventions around that.
Tableau offers power tools for doing all of the analytics tasks detailed above, from the District to the class level. Whether it is tracking trends, quickly identifying anomalies, or building predictive models Tableau can aid school districts in assessing student performance and planning interventions – allowing the end user to ask and answer questions of their own data.
Jonathan Rauh
Jonathan received his PhD in Public Policy with an emphasis in Quantitative Analytics focusing in Education and Health Policy. He has held multiple roles in education analytics including Research Director for eLearning for the SC Department of Education, Evaluator for multiple NSF Education Grants, and is the author of multiple articles on eLearning and the use of social analytics in K12. He has also served on the faculty at East Carolina University, College of Charleston, and Virginia Commonwealth University where he taught courses in Statistics, Business Intelligence, and Educational Policy Analysis. Jonathan is currently a Lead Solution Engineer with Tableau at Salesforce.
