Heuristics, national benchmarks, and frameworks for collecting, analyzing, and presenting program data to make persuasive, ethical arguments to administrators — grounded in real data from 435+ institutions.
Before collecting or presenting any data, work through these questions. They are not a checklist — they are a thinking framework for every stage of the advocacy lifecycle, from exigence to circulation.
Drawing on D'Ignazio & Klein's Data Feminism (MIT Press, 2020), this framework asks WPAs to attend to power at every stage of data work — not just what the data shows, but who produced it, for whom, and to what ends.
The DFW "problem" is often framed in ways that locate failure in students or instructors — not in institutional systems, resource allocation, or working conditions. The act of naming that frame is the first step to changing it.
Similarly, class size data is routinely presented as an efficiency metric. A feminist data approach asks who is harmed by that framing — and positions the same numbers as a student success and labor equity argument instead.
Real data from 435+ institutions collected through the CWPA community. Use these figures to contextualize your local caps against national practice — and against the CCCC professional standard.
| Course Type | N Institutions | Min | Max | Mean | Median | CCCC Standard |
|---|---|---|---|---|---|---|
| FYC / 101 — All Institutions | 417 | 12 | 39 | 22.3 | 22 | ≤ 20 |
| FYC / 101 — 2-Year Colleges Only | 101 | 18 | 39 | 25.4 | 25 | ≤ 20 |
| FYC / 101 — 4-Year Institutions | 286 | 12 | 35 | 21.2 | 22 | ≤ 20 |
| Basic Writing / Developmental | 216 | 10 | 35 | 19.3 | 20 | ≤ 20 |
| Second Semester / 102 | 174 | 15 | 30 | 23.1 | 24 | ≤ 20 |
| Online Sections | 83 | 2 | 35 | 22.4 | 23 | ≤ 20 |
When administrators raise DFW rates, your first move is to slow down the conversation and gather the data that tells a fuller story. This five-step framework gives you the structure to do that — and the language to make the case that a single DFW rate, without context, produces premature interventions that don't address root causes.
Before anything else: what is the national range for DFW rates in composition, for your institution type? A 22% DFW rate at an open-access community college is not the same problem as 22% at a selective 4-year institution.
Your first move is to ask for time to gather context — not to defend, not to promise fixes, and not to accept the premise that the rate is simply "high."
Before attributing DFW rates to curriculum or instruction, ask:
A single DFW rate hides enormous variation. Push for disaggregated data:
See Derek Mueller's "Silhouette of DFWI" for visualization approaches.
Quantitative data alone won't explain what's happening. Gather qualitative data:
The relationship between faculty working conditions and DFW rates is direct but routinely ignored in administrative conversations. Surface it explicitly:
The question to surface: How do our faculty working conditions affect our ability to provide the early intervention and feedback quality that prevents DFW grades?
This reframes the conversation: DFW is not primarily a curriculum problem or a student preparation problem — it is a resource allocation problem. The solution is not a new assignment sequence. It is smaller classes, better-supported faculty, and proactive student services.