Frame the course
Start with the course outline and other guiding documents, then turn the course requirements into a rubric with clearly defined evidence and rating levels.
Transparent methodology
OER Fit asks one practical question: how explicitly does this resource teach what this course requires? AI helps locate possible evidence, and a human reviewer checks the source before anything is published.
The dots describe course fit, not overall quality. They show how much meaningful, teachable coverage of a specific course criterion appears in a resource.
They do not certify a book, choose it for you, or turn the resource into one overall score.
The method is designed to be transparent enough to inspect and structured enough to replicate.
Start with the course outline and other guiding documents, then turn the course requirements into a rubric with clearly defined evidence and rating levels.
Apply the same rubric to likely or widely used OER. AI helps with scale, organization, and locating possible evidence.
Open the resource, verify the evidence, revise unsupported claims, and add human context before anything is published.
The criteria change by course, but the dots always describe the same thing: how much explicit, teachable coverage appears for that criterion.
Substantial, explicit instruction that can carry a meaningful share of teaching in that area.
Useful direct instruction, but some range, depth, or support may still need supplementation.
The topic appears, but treatment is brief, secondary, or not enough to teach it independently.
Little or no direct instruction in that area.
Three dots does not mean “best book.” Fit depends on the course and the resource's teaching role. The same OER may look very different in C1000, C1001, C1002, and C1003.
If you want to inspect how the review is built, open each section below. You do not need this detail to use the course pages.
The rubric is an analytical tool developed for this project. It is not an official CCN, C-ID, articulation, curriculum, or institutional compliance instrument.
AI is not the evidence. The OER itself is the evidence.
Another faculty member should be able to see the criterion, the rating, the source evidence, and any important limitation or supplementation need.
They answer different questions and should not disappear into one blended score.
Asks: What does this resource appear to teach in relation to this course?
It comes from the course rubric, source evidence, and the review process above.
Asks: What happened when someone actually taught with it?
Teaching notes can reveal sequencing, student response, accessibility barriers, assignment possibilities, and other realities an initial review cannot.
A practical shortcut for a resource that appears especially worth investigating early. It is not an award or official endorsement.
A practical judgment about whether a resource may work best as a main course text, readings collection, or supplement.
Deeper planning context for likely main course texts, such as useful chapter sequences, supplementation needs, edition differences, available platforms, or teaching materials.
Classroom observations contributed separately from the editorial course fit review so readers can see which kind of evidence they are reading.
OER Fit observations should not be treated as official CCN compliance reviews, curriculum approvals, articulation decisions, accessibility certifications, licensing audits, or endorsements by ASCCC, ASCCC OERI, the California Community Colleges Chancellor's Office, or an individual college.
Faculty remain responsible for reviewing resources in relation to their own course outline, local requirements, students, assignments, accessibility expectations, licensing, and instructional goals.
The course rubrics show what the method looks like when applied to current California Community College English courses. Use the accessible HTML grids to inspect each course rubric, compare criteria, resize text, and navigate with row and column headers.
The method is meant to be adaptable. Start with your own course documents, build and calibrate a rubric, use AI only for provisional initial analysis, and verify every substantive finding against the resource itself.