SIS SIG - Meeting 21 - 2026.2.25

Participants

Agenda

1. Ed‑Fi NACHOS Program
Objective:

• Better quantify implementation complexity

• Provide concrete recommendations to help states align more closely with core Ed‑Fi

• Reduce:

  • implementation variation

  • integration fragility

  • ongoing vendor support costs

2. Certification Automation

Slides:

Discussion

1. Ed-Fi NACHOS Program

Key points raised

• Aggregate vs. Granular Attendance Data: SIS vendors capture granular attendance data and prefer states calculate aggregates themselves, since aggregate rules vary widely by state and are difficult to standardize. Sending aggregate attendance increases payload size, processing overhead, and complexity without reducing integration burden. The group generally agreed granular data should be the long‑term standard, even if some states currently require aggregates.

• Data Trust and Program Office Alignment. Requests for aggregate data are largely driven by program offices not fully trusting granular attendance data, rather than issues with SIS systems themselves. Vendors emphasized that the data sent reflects exactly what districts enter, and trust gaps stem from policy interpretation and local practices. Improving alignment between state IT teams and program offices was identified as essential to reducing unnecessary extensions.

• Performance and Scale Implications. Aggregate and calculated attendance fields often cannot be event‑triggered, forcing nightly or bulk recalculations that generate large data volumes. Positive attendance models exacerbate this by requiring frequent updates as attendance status changes throughout the day. These patterns create scalability and performance challenges, especially for large districts.

• Membership and Residency Extensions (Enrollment Domain). Many states extend StudentSchoolAssociation to capture membership and residency, creating inconsistent and duplicative models across implementations. The group discussed moving these concepts to StudentEducationOrganizationResponsibilityAssociation (SEORA) with an added education organization reference. This approach would reduce extensions, align better with authorization constraints, and improve cross‑state consistency.

• State Commitment and Adoption Timelines. Vendors expressed concern about continued investment without clear state commitments to adopt recommended changes. States, including Wisconsin, indicated willingness to make changes, with several targeted for the 2027–28 school year. To address transparency concerns, Ed‑Fi plans to track and share state and Alliance commitment timelines alongside recommendations.

Consensus direction

• Granular attendance data is the preferred model

• States should:

  • Trust granular data submitted by SIS systems

  • Perform aggregate calculations themselves using state‑defined rules

• Aggregate attendance should not be the long‑term preferred model, even if it is less costly than other problematic areas

2. Certification Automation

   1. Current State: Ed‑Fi Certification Today

      • Ed‑Fi Certification Suite v2

      • Uses Bruno as the test client

      • Supports SIS v4 certification scenarios

      • Assertions and scripts populate values before and after API responses

      • Includes ad‑hoc scripts for quickly provisioning required resources

   2. Identified Challenges / Opportunity

      • Manual and ad‑hoc setup of development sandbox environments

      • Centralized setup model limits scalability

      • Fragmented certification stages delay feedback to application developers

      • Human expertise still required to validate semantics, even when field types and values are verified

      • Misalignment between:

        • documentation

        • certification requirements

        • data standard specifications

   3. Proposed direction: Automation

      1. The proposed direction is to move Ed‑Fi certification from a mostly manual, expert‑driven process to a more automated, scalable one—without removing human oversight where it still matters. Key ideas:

         1. Make certification part of standard admin tooling, not a separate, specialized workflow

         2. Align standards, documentation, and certification rules so they no longer drift apart

         3. Automate the setup and validation of required resources

         4. Reuse standardized certification components so new domains and scenarios can be added faster

      2. The goal is to: reduce setup effort and delay, shorten the feedback loop for vendor, improve consistency across certification experiences

   4. At a high level, the automated model shifts certification from a one‑off process to a repeatable, tool‑driven workflow.

      1. In practice:

         1. An Admin App provisions:

            1. Ed‑Fi instances

            2. Application credentials

         2. An Admin API triggers certification tests programmatically

            1. The Bruno test suite:

               1. Is invoked via SDK or CLI

               2. Is no longer tightly coupled to the Bruno UI

            2. Certification results are produced as a final report. Still reviewed by a human before approval

            3. Additional concepts introduced:

               1. Universal IDs are used for certification

               2. Certification results are issued as Verifiable Credentials (VCs) using DIDs. This allows certification status to be verified cryptographically by downstream systems

                  Importantly: Vendor developers use the same tooling as service providers

               3. Automation reduces friction, but does not remove human judgment entirely

Action Items

• Ed‑Fi Alliance

  • Engage with Wisconsin IT and program offices to build trust in granular attendance data

  • Advocate for state‑side calculation of aggregate attendance values

  • Track and communicate state commitments and timelines for recommended changes

  • Raise membership and residency model changes in:

    • SEA workgroup

    • Data Standard workgroup

  • Assess feasibility of non‑breaking changes to SEORA to reduce enrollment extensions

• SIS SIG Participants

  • Provide feedback on proposed SEORA changes in upcoming Data Standard workgroup meetings

  • Continue identifying “big rock” complexity drivers for prioritization

  • Validate whether proposed NACHOS recommendations meaningfully reduce SIS implementation burden

Open Questions:

• How can states more effectively validate and trust granular attendance data?

• Which NACHOS recommendations should be prioritized based on state commitment likelihood?

• What additional enrollment‑related extensions could be eliminated through SEORA evolution?