1. Develop a solution for tracking assessments and data.

In its most constrained format, with one baseline assessment and one summative assessment for each course, most K-12 districts will require a data system that can handle at least 500 unique assessments. Districts that require multiple measures or that allow for a wider range of assessments have an exponentially larger management challenge.

Further, each course has multiple teachers who each may select a slightly different content focus for their SLO, resulting in thousands of assessment configurations that must be managed, analyzed, and reported upon. Student results for each assessment and item groupings must also be maintained. A data system that can house this information in a relational database and provide an intuitive user interface is essential for this volume of information management.

2. Devise a method for managing data associations at scale.

As stated above, each Student Learning Objective is associated with a baseline measured by at least one pre-assessment, and a student growth target measured by at least one post-assessment. A growth algorithm connects these two assessments and identifies the percent change or static change required to meet the SLO.

To ensure an SLO has practical use, content of the pre- and post-assessments should be aligned, and educators should easily be able to view assessments and their attributes side by side. To facilitate this work, a district needs an ed-tech solution that allows for selection of compatible pre- and post-assessments, matching of items from pre- to post-assessment based on content and rigor, and simple use and customization of growth algorithms.

3. Facilitate workflow processes to streamline management.

As Student Learning Objectives are implemented, pre- and post-assessments will be administered in varied ways. District data systems should allow for plain paper scanning, selected response scanning, and integrated online testing, so records are maintained and scores are available automatically for analysis.

For assessments not scored within the district’s data platform (such as standardized test scores), the system should have the capacity to load results in multiple formats, including XML, API, or a common flat file layout. Otherwise, external results will have to be processed manually, which is time consuming and can compromise data integrity.

Results should be housed in a relational database that provides an administrative interface with access to individual student data points as well as SLO metadata. SLO metadata are the attributes associated with the given Student Learning Objective, such as the growth algorithm, students included in the SLO population, baseline scores, and derived target scores.

Finally, the ed-tech system should have the capability to roll up student results into a final SLO score for each educator and integrate this score into multi-measure evaluations, producing a final evaluation rating for educators.

4. Create an audit trail.