Guide to Using District Data in Improvement Efforts

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Using data to improve schools involves more than just collecting it. Having data at hand is still not enough to make it work. To be able to draw valuable insights, you need to manage and analyze the available data to identify certain patterns and implement relevant changes based on your findings.

In this article, we’re going to discuss how to streamline existing data and use it for its ultimate purpose, which is to improve the performance of students and the school as a whole.

Steps to develop a data-driven improvement plan

To ensure effective evidence-based practice, the data used to create a school improvement plan must be found, analyzed, and utilized. However, that needs to involve a process, for which the following specific steps must be implemented: first, the goals must be identified, then goals need to be defined, then the data must be collected. Once the data is already gathered, it’s time to cleanse it, aggregate it, analyze it, and explain the findings to arrive at proposed improvements.

  1. Identify the goals

    What are the goals that you seek to achieve? Where are the areas of improvement? Where are the areas of success? Are there targets that have to be met? Are they final or stepped, where progress has to be shown every year?

  2. Define the processes

    Once the goals are identified, then the processes surrounding the data must be defined. How will data be collected? Is it in a system like a student information system? Are there external sources that need to be brought in? How will that process work? Can it be automated? Who are the data stewards, who will “own” the data, both from a technology and a business perspective. Will they be the same people? How will they be trained to act as such? Where is the data stored? Who has access to it? What are the data safety, security, and confidentiality aspects of the process? The reality frequently is that limited resources means that districts are not able to fully take advantage of the data, but implementing processes that seek to enhance the availability and use of data is a solid step in data-driven decision making.

  3. Collect the data

    With the processes defined, the collection of the data can begin. The most common sources are systems like student information, online learning resources, and third-party evaluation tools. The advantage of this is that data collection is automated, and usually does not require double data entry. For example, by using rostering capabilities built into many systems, districts can share student lists with ancillary systems, from bus and food services, to those online learning and evaluation resources. Additionally, technology has improved the ability to share data across systems, and there are several tools that can help with the analysis.

  4. Aggregate and analyze the data

    While data in its own source systems can provide some reporting and analysis, it can be laborious and difficult to compare results across systems or collate it into a single tool for analysis, especially when it comes time to try and identify patterns and correlations in data sets that sit in different systems or silos. Still, building on the reports identified as part of the process definition, districts can use both native reporting capabilities found in their various systems, as well as external analysis tools, from Excel to generic analytics platforms and specialized tools specifically designed for education agencies to create and publish reports to those who need that information, from district leadership, to staff, students, parents, and the community at large.

  5. Interpret the findings

    While analytic tools provide a great deal of insights, the final interpretation of the data really needs to occur with human intervention. For example, analysis can tell you that there was a dip in student performance and attendance, but those in a district will have to evaluate how those might have been affected by things like natural disasters, or other events which disrupted teaching and learning. The combination of the insights from the analytics tools and the experience of those interpreting the data is the right combination for validating, or disproving, anecdotal evidence, strong impressions, and accepted “wisdom.”

Which role needs what type of data?

Data-driven decisions in education are made on various levels of the entire system, thus data is a necessity at every level of education. Detailed information, whether about an individual or a group can help those making decisions to see areas of need and success, are critically important when evaluating what steps to take to improve student outcomes.

This pattern works from the most impersonal level, at state and federal government departments of education, who frequently use data to determine how funding is provided to address school issues. Local authorities need a different, more detailed data set, as they are responsible for decisions about employment, school expansion, as well as, usually, the entity ultimately responsible for students.

Another layer down, we find principals, and other school personnel, who need data both for groups of students, but also individuals, as they straddle the line between administrators who manage organizations, and staff who interact with students. Finally, specific decisions concerning individual students are made by classroom teachers, the pupils’ guardians, and even students themselves. They each need information about what is happening, and each of them offers their lens in interpreting that data, which can provide alternate pathways for how to proceed. For example, a teacher knows that a student did not complete the assignment, but the parent is aware that the student completed and has the email showing that it was sent on-time but to the wrong answer. That information can influence how the teacher treats the missing assignment.

Each of these stakeholder groups needs a different level of detail because of the different focus. National-, state-, and district-level officials rely on examination results and formal assessments to analyze and modify the existing system, because they are evaluating performance for large groups of students, while those in schools need much more detailed information, such as, student attendance, performance, teaching standards, degree of curriculum completion, and results of state tests and evaluation tools. Parents considering alternatives for their children’s education look at metrics like graduation rates, school environment and safety, diversity, staffing, and extracurricular activities.

Regardless of the level, having data easily available, that answers questions at the right level, and with the right context, is important.

Challenges in implementing data-driven school improvement

But even with all of the above steps being enshrined a good data plan, with clear governance, and excellent report definitions can run into several issues that hinder to build an extensive body of data and background information to make informed decisions. Below are the most common ones.

Data availability

Lack of available data or delays in publishing the required information is a major obstacle to implementing timely, efficient interventions. Insufficient data results in inaccurate findings and imperfect decisions. That is why a data plan is a critical step. By identifying what questions should be answered by data, we can begin to identify if, and where, that data exists, or if not, how we might go about collecting it.

Access and accessibility problems

There are two key issue here, access and accessibility. The first is can the data that is necessary for decision making be accessed either by the decision makers themselves or someone who can provide it to them? Privacy and security are important safeguards but can also serve as roadblocks to that access. Part of any data plan should include governance and policies that define the version of “need to know” type of access to data. Any such governance needs to adhere with various data guidelines, from federal laws, such as the Federal Education Rights and Privacy Act (FERPA), to international laws such as the European Union’s General Data Protection Regulation (GDPR), to state statutes and and local or municipal information technology guidelines.

The second issue is accessibility. This can refer to either how easily the data can be viewed, absorbed, or otherwise incorporated into decision making, or if it follows interoperability standards that help migrate it from system to system. For example, if a fifth-grade teacher has students using three online evaluation tools, while each of the tools might provide excellent reporting for its own findings, combining that data into a singular view that makes seeing a more wholistic view of the students’ performance might be difficult if those reports are not available in an easily exportable format, so that they can be viewed side-by-side.

Quality issues

Even when the required data is available, there is always the question of data quality or relevance. While it is possible to tell when the data is untimely or incomplete, assessing data quality can be more difficult. From simple data entry errors, to system issues, data quality can be affected in ways that not only hinder its use, but can provide incorrect inputs that lead to decisions being made using incorrect data.

Limited capacity and skills to use the data

Resources are limited. Everywhere. The lack of either data analysts and stewards who can help process and analyze the data is a reality faced by schools and districts throughout the country. Furthermore, professional development to incorporate data into leadership’s decision making or into teachers’ practice can often be a luxury that is not affordable in terms of time and/or money. While not a panacea, technology can address many of these issues, by providing automated data migration, aggregation, and analysis. Furthermore, good systems provide visualizations that are succinct and successful at extracting the insights and actionable information that exist in the raw data.

Improving teaching and learning

In order for data to provide assistance in identifying areas of need that need to be addressed, and areas of success to be replicated, including a visible improvement in teaching and learning, the complete cycle of planning, gathering, cleansing, analyzing, and providing data to decision makers in a way that they can easily make use of it must be followed and revisited. It is common that as the initial goals for data are met, stakeholders come up with more sophisticated, more complex questions that data might be able to answer. For the process to be effective, it cannot be a one-off action, but a continuous effort, building a data culture at school. AnalyticVue can help educational facilities collect, manage, and interpret data so that professionals, parents, and student can make informed decisions about what resources to use to help students succeed.

AnalyticVue case studies

A customizable system for organizing K12 data is an asset when you’re planning a long-term improvement strategy. A number of schools have chosen AnalyticVue to support them in the process of managing data to obtain actionable insights. It was especially important after the turbulences caused by the COVID-19 pandemic when schools had to adjust their approach and keep a close eye on the students’ performance, even when classes were held online.

Using data to improve schools – what’s working?

To be sure that you’re using the data in the best way possible, you need a solid framework to rely on. A good system will not only facilitate data collection but will also help you manage this data to make the most of it. Properly classified and analyzed data will produce insights to drive targeted improvement plans, so that everyone involved with a student, including students themselves, can help decide how to help them to be successful.

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