Schools must contend with immense volumes of data when planning MTSS initiatives, but student data walls can help them zoom in on just the information they need for the task at hand.
Data-driven decision-making has proven to be an effective way to support students by improving the accuracy and foresight with which their individual needs are assessed.
The popularization of the response to intervention (RTI) approach was pivotal to this improvement, as it enabled teachers to better understand and address specific students’ needs by organizing them into a clearly defined three-tier academic support system. RTI has since evolved into multi-tiered systems of support (MTSS), an approach to intervention that builds on its predecessor’s tiered model by considering all parts of a student’s life — not just academic performance.
To adopt the more panoramic perspective MTSS demands, educators must aggregate a wide range of student data, including attendance patterns, disciplinary track records, standardized test scores, and academic histories. However, doing so has traditionally been as difficult as it is important. Many educators have adopted data walls to track large groups of students, but this solution is often only useful for tracking a single metric (typically assessment scores). To help educators consider the whole child and streamline this process, we’ve developed a more comprehensive digital student data wall on our platform.
Overwhelming volume is among the primary obstacles to effective data use in K-12 settings. In the wake of ESSA, schools have had to double-down on collecting copious data, making it difficult for educators to distinguish signals from noise.
By using our student data wall, teachers, intervention specialists, principals, and other stakeholders can view, manipulate, and zoom in on only the information that is pertinent to the task at hand. Digital data walls provide educators with sophisticated dashboards that aggregate and organize student data into easily decipherable tables. These customizable tables have many use cases, but are particularly helpful for performing longitudinal analyses of key datasets and combining datasets in ways that facilitate the identification of trends across a classroom (or other relevant grouping of students).
For example, if a teacher is trying to identify a specific group of students — say, students testing poorly in math — they could use their data wall to quickly pull up recent math assessment scores and isolate students whose performance fell under a certain threshold. Once these students are grouped together, the teacher could use the data wall to explore whether these students share other behaviors or characteristics that might be the common cause of their struggles. Perhaps some of the students are missing an unusually high number of class sessions, preventing them from absorbing the material as thoroughly as their peers; perhaps others just had a bad day during the last testing period.
Whatever the case may be, using wide-ranging, dynamic datasets to compare these students will yield more precise insights into which students are improving, which are moving in the wrong direction, and why. Leveraging the Hoonuit data wall for comparative analyses like these can even help teachers reevaluate their placements of specific students in MTSS tiers.
Digital data walls aren’t just reserved for the classroom — principals and other administrators stand to gain from their use, as well. By providing a big picture overview of a student body’s performance, a data wall can help a principal identify overarching challenges in their building and start the process of determining the root causes of these challenges.
For instance, by looking at their data wall, a principal might discover that there is a large contingent of third-graders struggling with reading comprehension. Upon applying filters to the data wall to zoom in on the contingent in question, the principal might find that this issue is directly correlated to poor attendance or a particular disciplinary trend.
Armed with this depth of knowledge, the principal would be able to design and implement school-wide initiatives that are tailored to their student body’s unique needs, not merely a reflection of generic best practices.
Ultimately, without access to data dashboards that are at once as comprehensive and manipulable as Hoonuit’s data walls, performing the kinds of analyses outlined above is incredibly tedious. It involves sorting through endless piles of individual student profiles, cross-checking data to try to find commonalities, and manually updating incorrect records — all told, an often prohibitively inefficient process. In practice, this means whole child evaluations are done sparingly — perhaps only annually — instead of being used as a powerful daily tool by teachers, support staff, and administrators.
In short, data walls make it easier for schools to implement any number of diverse student and school intervention initiatives. Better-organized data leads to better-tailored student supports, which in turn generate better outcomes on both an individual and school-wide basis. It’s fair to say this is one wall by which educators should want to be backed.