Wrap Up Data Processes

End of School Year

Wrapping up data processes for technical systems at the end of the school year is an important step for student success teams, allowing teams the chance to adjust technical systems to improve technical and human workflows, reporting capabilities, systems interoperability, and threshold monitoring. This process necessitates a technical infrastructure that has the capability to automatically display and flag students at risk of falling behind on courses or not graduating alongside the ability for summer school staff to access student profiles. By completing the Optimal Technical Infrastructure Checklist, teams can move beyond manual data translation and make sure their technical systems are set up to archive holistic end-of-year data while also supporting summer school by setting automatic reminders to follow up on summer school enrollment. Ultimately, planning for changes to technical systems when paired with an optimal technical infrastructure allows for seamless archiving of student profiles, translating current year data to historical data, verifying final credits and status, managing exiting and transitioning students, and assigning summer or next year supports.

Technical Infrastructure Choose Plays Examples in Practice

Technical Infrastructure

Before diving into the processes involved in Wrap Up Data Processes, we encourage your team to ensure your technical infrastructure is ready for this effort. In the case where you aren’t sure, this checklist will generate a set of questions you can ask the district or provider for clarity. In the case where you know the system(s) cannot do something, this checklist offers considerations for building workaround solutions.

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Data Integrity & Governance

Is your system set up to archive holistic end-of-year data?

System Configuration & Customization

Is your system set up to allow summer school staff access to student profiles?

Actionable Analysis & Logic

Is your system set up to automatically flag students who are missing required courses or are otherwise off course for graduation?

Workaround Option 1:
If there is no district-provided graduation tracker, export accumulated course credits into Excel (and, if possible, credits by subject and/or core vs elective classes) for each student. Include credits required and calculate the difference. Export any other aggregate graduation-related data available (e.g., physcical education requirements, required assessments) and merge the data into the spreadsheet with credits accumulated. Sort by accumulated credits and student’s grade. Focus first on students who do not have the expected number of credits.
Workaround Option 2:
If graduation-related data is tracked (e.g., student profiles contain data such as “credits remaining” or specific courses needed), but there are no summary views or reports, export the data (by value, if there is no multi-variable table available) into an analytics and reporting tool. Sort by accumulated credits and student grade. Focus first on students who do not have the expected number of credits.
Ai-enabled workaround:
If graduation-related data is available but in different systems (e.g., course credits are in one systems and state assessment data in another), export the data system-by-system into an analytics and reporting tool. Sort by accumulated credits and student grade. Focus first on students who do not have the expected number of credits. If the district has a closed and secure AI tool and allows the upload of student identifying information and data, export the raw data and prompt the AI tool to visibly flag (e.g., add colors) or add a column to capture the specific issues that need to be identified and resolved. Consider including instructions for how outlier data values should be treated (e.g., if there is a null value, treat it like a zero). Prompt the AI to explain the steps taken with the data to produce the output requested and to share any inconsistencies, risks, or issues with the data and how they have been resolved or attended to in the resulting report. If any of the explanations are not clear or do not make sense, prompt the AI to alter the approach it takes to flagging the data. If the report is rerun, prompt the AI to call out the specific changes in each iteration from the prior version(s). Remember to at least spot check the data against the source data to ensure accuracy. Ensure responsible use of AI by referring to the Considerations for AI Use section of this playbook

Questions for District:

Does the system automatically understand equivalent credits within subject areas? Does our SIS’s graduation logic use “course tags” or “course codes”?
Can we access a single dashboard that displays both the live graduation indicators or graduation audit with the scheduler tool?
How do we map course equivalencies across our graduation requirements, including for transfer and CTE students?
Can the SIS generate a report that indicates gaps between current enrollments and missing requirements?

Questions for Provider:

Can the SIS generate a report that indicates gaps between current enrollments and missing requirements?
How can I use our SIS to identify course enrollment gaps for students that would prevent them from graduating on time? How do the filters reconcile “in progress” vs. “completed” credits?
Does the platform’s graduation audit engine support subject-level logic that can streamline mapping each course code to subject-area tags?

Responsive Communication & Alerts

Is your system set up to send automated reminders within relevant time boundaries to follow up on summer school enrollment packets?

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