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Strategic Planning with Personalized Analytics

Skyward Best Practices
Skyward Best Practices - Experience, Collaboration, and Leadership

***Editor’s note: In keeping with commonly accepted modern conventions, the word “data” will be used to represent both the singular and plural contexts of the word. As a result, you can expect to see the phrase “data is” instead of “data are” throughout most Skyward content. For more information on the grammatical debate, we recommend this 2012 article from The Guardian.
 

The collection, storage, and analysis of educational data has been a hot-button topic in the education community for several years. In many schools, the types and amount of data that are being collected have not changed as much as the way this data is stored and used.

Information that was once kept in filing cabinets stuffed with overflowing manila folders has been relocated to electronic mediums and, more recently, the cloud. As a result, data has become more accessible to those who benefit most from having the information at their fingertips. This sounds great in theory, but only a small percentage of school district staff – be it superintendents, business managers, or teachers – have a firm understanding of how to properly use that data.

Low data literacy levels are not the fault of educational certification programs, district leaders, or teachers. Technology integration has outpaced the learning curve, and professional development resources are scrambling to keep up. At the same time, research throughout this decade has consistently revealed a correlation between student achievement gains and effective data use. It can be inferred that those who are working to instill a data-friendly culture now are putting themselves in the best position to realize sustained improvement over the long term.

Educators, support staff, and district leaders will adapt. As data analysis evolves from a novelty into an expectation, educational data systems will come to be seen not as confusing monstrosities, but as valuable tools that can be tightly integrated with operational and pedagogical practices. Application and analysis will be representative traits of this next step in the evolution of data literacy. But where will we go from there?

Skeptics have been quick to point out that students are more than just numbers and trends. Grades, attendance, and test scores provide – at best – an incomplete and often inaccurate portrayal of a student’s proficiency, to say nothing of their personality and character. In the wake of initiatives that aim to evaluate teachers based on student performance, much of the pushback is centered on the idea that student outcomes are lacking in context and not always indicative of teacher effectiveness. These viewpoints both have merit, which is why the future of educational analytics will be awash in collaboration, personalization, and continuous improvement.

Data-driven decision making in K-12 is not about blindly following the numbers. It’s about finding the best way to weave those numbers into processes and practices that already exist in order to help our schools better serve their communities. Here are four steps that school district leaders can take in order to create a data-friendly culture and promote the collective evaluation and synthesis skills necessary for impactful eddata usage:




 

Ongoing Professional Development

Level 1 (Baseline)

When districts first implement a data analytics solution, users receive training on the basics – logging in, finding and sharing reports, selecting bounds and variables, etc… Technical staff might learn how to set up role-based dashboards so staff will have easy access to any number of built-in reports. Experienced vendors should be able to provide recommendations and configuration to get the district off on the right foot. Training is complete when all users understand system basics.
 

Level 2 (Best Practices)

The question of “how does data literacy help me be more efficient and improve the outcomes of my stakeholders” should be the underlying theme of the learning process, lest we forget the primary purpose behind data collection and analysis.

Knowing how to use technology and understanding how to make it work for you are two very different concepts. The first step to creating a data-friendly culture is to provide multiple learning options, including follow-up and meaningful feedback for school staff, above and beyond vendor-provided training. This requires buy-in from all stakeholders and strong district leadership. When training and resources are made available to the community, parents are more likely to remain involved in their child’s education.

Learning is not a one-time event in these progressive districts, where data literacy is an ongoing focus of professional development and new staff members receive the same, quality training as those who were around for the initial launch. Incentives can be made available for those who achieve tiered levels of data proficiency. Vendors with continuing education and rigorous mastery certification offerings are strongly preferred in this environment.


 

Collaborative Strategies

Level 1 (Baseline)

The first stage of strategic data use is to identify the ways in which existing information and standard reports can inform an individual or organizational approach. District Business Managers might use data for budget forecasting or to inform contract decisions. Teachers, on the other hand, can routinely review attendance and grading reports to identify at-risk students and provide intervention.
 

Level 2 (Best Practices)

The best data strategies put a heavy emphasis on collaboration and communication. District leaders can use this step to involve their communities and lay the groundwork for a transparent, positive, and consensus-driven data culture.

Instead of relying solely on “standard” reports, data-driven school districts work together to define custom strategies based on strengths and improvement opportunities, then adapt their technology to fit the strategy, rather than allowing the systems to dictate their approach.

Parent-teacher communication thrives when innovative educators include families in the planning process by soliciting input as to the best method and frequency for data sharing and interpretation. By working in tandem, parents and teachers identify individual goals and tailor the data review process accordingly. Supporting technology should make it easy for any user to set up custom formulas along with automated event notifications based on user-defined thresholds without programming knowledge. 


 

Sustained Support

Level 1 (Baseline)

Technical support is a basic expectation of any analytics solution. The technology itself should never be a hindrance to the goals of such an important initiative. Quality support depends on a combination of well-trained internal staff and responsive vendors. As a result, it is vitally important for any technology at the heart of a data-driven culture to be properly vetted during the procurement process and maintained by a proven firm with strong ties to the education landscape. 

What is the average response time to a service call? What do your response and resolution numbers look like over the past year? These are important questions to ask before signing any contract.
 

Level 2 (Best Practices)

The theme of “we are smarter together than alone” is central to a healthy data support model. The best ideas typically arise and evolve in strong user communities. A data-driven culture is about supporting one another, and peers make for the best resource.

On the school level, users actively engage in a forum to share ideas, while data practices are on the agenda at every department meeting. On a larger scale, user communities are active and engaged at the district, state, and even national level. Templates and ideas are shared across multiple mediums, from district message boards to user groups, and even Twitter #hashtags

Next-level support is about more than just troubleshooting technology. Leaders of a data-driven culture reinforce training by mapping it to human applications. It’s the difference between learning a fact for a test and understanding how to turn that factoid into actionable knowledge. Principals review data strategies with teachers to determine what is and is not making an impact and identify areas where data can inform instruction and professional development. Power users of the analytics system support department leaders by collecting feedback and meeting at regular intervals to determine any changes that need to be made.


 

Perpetual Review

Level 1 (Baseline)

Today, district leaders use data to balance budgets, create staffing plans, and review community engagement, among many other concerns. Tech directors review usage statistics to determine infrastructure needs and initiative status. HR directors have data-driven position control methods in place to ensure appropriate staffing. School administrators review teacher effectiveness models and student outcome trends to determine their best course of action. Teachers who have incorporated data into their pedagogy use graphical trends in conjunction with observation and feedback to reflect on interventions and classroom strategies.

There are few roles within the school district that do not incorporate some aspect of data review into their routine already.
 

Level 2 (Best Practices)

The pursuit of incremental improvement can have a positive cumulative effect, or it can distract from larger issues that deserve more attention. Perpetual review, when combined with strong data literacy, can help school districts stay on the path of improvement and avoid the perils of the white rabbit.

The difference between traditional data review models and those that we find in data-driven environments is subtle. The scope of review in the latter districts is expanded to include collection and reporting processes rather than focusing on the output.

Data analysis is always tied back to tangible outcomes – the point of data is to inform decision making, not create handouts, after all. Is operational data integration producing a noticeable return on investment, given the time and effort that must be invested? Are student outcomes improving in the data-driven classroom? Are parents overwhelmed or appreciative of data sharing practices? 


There may be circumstances in which “more data” is not the right solution, and there may be others that require broader, more frequent analysis. As a result of this review, district leaders should be able to determine whether a given practice is moving the needle, and, if so, at what magnitude? With this information in hand, districts can plan for the next round of staff development, and the data-driven culture cycle begins anew. 




 

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