Data and Visualization · Resource 2 of 4
Data-Driven Advocacy Guide
Heuristics, national benchmarks, and frameworks for collecting, analyzing, and presenting program data in persuasive and ethical arguments to administrators, grounded in course cap data from more than 435 institutions.
Use this whenAn administrator raises DFW rates, questions your class sizes, or asks you to justify resources. Use it before those conversations as well.
Planning and Visualization Heuristics
Work through these questions before collecting or presenting data. They are a framework for thinking at each stage of advocacy, from exigence to circulation, and not a checklist.
Heuristic 1 · Planning your advocacy
Questions to Ask Before You Start
- What is happening? What is the exigence for this advocacy, and why now?
- What is your goal? Which genre does your audience expect: a report, a dashboard, a one-page brief, a presentation?
- Who is your audience, and which data is legible and credible to them?
- Where did this data come from? Which biases and limitations should you acknowledge?
- Who is available to help with data collection and analysis: institutional research, the registrar, faculty governance?
- Which scholarship and national standards anchor your case?
- Are you making a comparison? Which benchmark is appropriate: national, sector, or peer institution?
- What are the limitations of your data, and how will you name them?
- Which terms are legible to your audience: enrollment, student credit hours, retention rate, DFW rate?
- What is your circulation plan? Who sees this, when, and through which channel?
Heuristic 2 · Class size case study
Visualizing Class Size Data for Different Stakeholders
- For campus leaders: a dashboard showing average class size by department, overlaid with cost and student credit hour data. Present caps as a factor in retention as well as a labor issue.
- For faculty governance: class size distributions by course type and modality, with student learning outcomes beside cap size. Show the learning rationale.
- For public advocacy: a comparison of class size before and after a change, set beside retention rates or student satisfaction scores. Use the national distribution below.
- Equity check: do low-enrollment sections such as independent studies, honors sections, and internships distort your averages? Disaggregate by section type before computing means.
- Transparency rule: every visualization states its data source, its collection date, and its known limitations.
- Framing principle: present class size as a condition of student success, and cite the CCCC standard as disciplinary authority in place of personal preference.
Key principle
Match your data to administrative legibility. "Student credit hours" and "students per section" describe the same sections in different terms. Know which term your dean uses.
Data Feminism Framework
Drawing on Catherine D'Ignazio and Lauren Klein's Data Feminism (MIT Press, 2020), this framework asks WPAs to attend to power at every stage of data work: what the data shows, who produced it, for whom, and to what ends.
Core principle
Data Is Never Neutral
The DFW "problem" is often framed in ways which locate failure in students or instructors, and not in institutional systems, resource allocation, or working conditions. Naming the frame is the first step toward changing it.
Class size data is routinely presented as an efficiency metric. A feminist approach to data asks who is harmed by this framing, and it presents the same numbers as an argument about student success and labor equity.
Questions for every stage
- Who collected this data? For which purposes, and at whose expense?
- Whose goals are foregrounded: retention metrics, cost reduction, student learning?
- Who benefits from the current framing of this data, and who does not?
- What is not counted? Which working conditions, student needs, and structural factors are invisible in the metric?
- Who is harmed by the dominant interpretation of this data?
- Whose labor produced this data, and are they credited?
Five Principles for Program Data
Examine Power
Data analysis and presentation are not neutral acts. Ask who holds the power to collect, interpret, and act on data, and who does not. Institutional research offices, provosts, and accreditors do not collect the data WPAs need.
Rethink Binaries and Hierarchies
Administrative data systems flatten complexity: pass or fail, enrolled or not enrolled, retained or not retained. Advocacy work should surface the variation within those categories, especially by race, income, first-generation status, and modality.
Elevate Emotion and Embodiment
Pair quantitative data with qualitative accounts, with explicit permission. A statistic about DFW rates is abstract. A faculty member's account of week 10 in a 30-person section is concrete.
Question the Default
The default in most institutional data is the full-time, traditional-age, residential student. Check whether your data is being read through this lens, and name it when it is.
Consider Context
A 25 percent DFW rate at an open-access community college serving many working adults differs from a 25 percent rate at a selective residential university. Context is the analysis, not a caveat attached to it.
These five are adapted from the seven principles of Data Feminism. Four carry the book's names. "Question the Default" is an addition for institutional data. The book's other principles are challenge power, embrace pluralism, and make labor visible.
National Course Cap Benchmarks
Course cap data from more than 435 institutions, gathered through a community-sourced collection across institution types. Use these figures to set your local caps beside national practice and beside the CCCC professional standard.
- 22National median cap, first-year composition2 above the CCCC maximum of 20. 417 institutions reporting.
- 25Two-year college median cap5 above the CCCC maximum. 101 two-year colleges reporting.
- 37%Institutions at or below the CCCC maximum152 of 417 institutions (36.5 percent) report a cap of 20 or lower.
- 25Most common single cap92 institutions (22.1 percent). 25 percent above the CCCC maximum.
Course Cap Statistics by Course Type
| Course type | Institutions | Minimum | Maximum | Mean | Median | Median vs. CCCC maximum |
|---|---|---|---|---|---|---|
| First-year composition (101), all institutions | 417 | 12 | 39 | 22.3 | 22 | 2 above |
| First-year composition (101), two-year colleges | 101 | 18 | 39 | 25.4 | 25 | 5 above |
| First-year composition (101), four-year institutions | 286 | 12 | 35 | 21.2 | 22 | 2 above |
| Basic writing or developmental writing | 216 | 10 | 35 | 19.3 | 20 | At the maximum |
| Second-semester composition (102) | 174 | 15 | 30 | 23.1 | 24 | 4 above |
| Multilingual or ELL writing | 37 | 12 | 28 | 18.5 | 18 | 2 below |
| Online sections | 83 | 2* | 35 | 22.4 | 23 | 3 above |
* The online minimum of 2 appears as reported and is likely an entry anomaly or a tutorial format. Treat the online range with caution.
First-Year Composition Caps: Where Institutions Cluster
| Cap | Institutions | Share | Cumulative share | Relation to CCCC standard | Bar |
|---|---|---|---|---|---|
| 12 | 3 | 0.7% | 0.7% | At or below the preferred size of 15 | |
| 15 | 19 | 4.6% | 5.3% | At or below the preferred size of 15 | |
| 16 | 14 | 3.4% | 8.6% | Below the maximum | |
| 17 | 8 | 1.9% | 10.6% | Below the maximum | |
| 18 | 29 | 7.0% | 17.5% | Below the maximum | |
| 19 | 21 | 5.0% | 22.5% | Below the maximum | |
| 20 | 58 | 13.9% | 36.5% | At the maximum | |
| 21 | 3 | 0.7% | 37.2% | Above the maximum | |
| 22 | 53 | 12.7% | 49.9% | Above the maximum | |
| 23 | 19 | 4.6% | 54.4% | Above the maximum | |
| 24 | 40 | 9.6% | 64.0% | Above the maximum | |
| 25 | 92 | 22.1% | 86.1% | Above the maximum. Most common cap | |
| 26 | 11 | 2.6% | 88.7% | Above the maximum | |
| 27 | 12 | 2.9% | 91.6% | Above the maximum | |
| 28 | 7 | 1.7% | 93.3% | Above the maximum | |
| 30 or higher | 22 | 5.3% | 98.6% | Well above the maximum |
The itemized rows account for 411 of 417 institutions (98.6 percent). The source file does not itemize the remaining 6.
Key finding for advocacy
The most common first-year composition cap nationally is 25, which is 25 percent above the CCCC maximum. The national median is 22.
These figures show administrators a sector-wide pattern. The professional standard is widely unmet, and a case for change is therefore a systemic argument and not a report of one program's failure. A cap at the national median still exceeds the standard.
Source: Community-sourced national course cap dataset, 435+ institutions, compiled by Holly Hassel and colleagues, via The Writing Program Exchange. Data quality: the dataset is community-sourced, and institutions categorize courses inconsistently. Compare within institution type (two-year or four-year) where possible.
Compare Your Program
Enter the caps you know and leave the rest blank. The comparison appears below and prints with the page. Nothing you enter is saved or sent anywhere.
DFW Contextualization Guide
When an administrator raises DFW rates, slow the conversation and gather the data for a fuller account. A single DFW rate without context produces premature interventions which miss root causes. These five steps give the conversation a structure.
Slow Down and Contextualize Nationally
Ask first for the range of DFW rates in composition at institutions of your type. A 22 percent rate at an open-access community college differs from a 22 percent rate at a selective four-year institution.
Your first move is a request for time to gather context. It is not a defense, a promise of fixes, or acceptance of the premise of a "high" rate.
Assess Your Student Support Infrastructure
Before attributing DFW rates to curriculum or instruction, ask what the institution provides.
- Which general academic support is available, and how do students reach it?
- Which reading and literacy support exists?
- Is the writing center adequately staffed, and is its outreach proactive or reactive?
- Is library and research instruction integrated into courses?
- Which support exists for multilingual students?
- Does the institution connect students to support, or wait for self-referral?
Disaggregate Your DFW Data
A single DFW rate hides variation. Request disaggregated data. See Derek Mueller's "Silhouette of DFWI" (opens in a new tab) for approaches to visualization.
- By course type: 101, 102, developmental, co-requisite
- By modality: face-to-face, online, hybrid
- By student population: first-generation, Pell-eligible, multilingual
- By section cap: 20 or lower, 21 to 25, 26 or higher
- By instructor appointment: tenure-line, full-time lecturer, adjunct
- By schedule and format: early sections, accelerated sessions
Gather Faculty and Student Perspectives
Numbers alone do not explain what is happening. Gather qualitative data.
- Faculty survey: at the end of the term, ask instructors to explain anonymously why each student with a D, F, or W received the grade, and which resources were available for referral.
- Faculty focus groups: which patterns do instructors see? Which support are they able to provide under current workloads?
- Student survey: ask students who received DFW grades about barriers, about what would have helped, and about what the institution did not provide.
Connect DFW to Faculty Working Conditions
Administrative conversations about DFW rates seldom include working conditions. Put the following questions to your own data before the meeting.
- How many writing students does each instructor teach per term, and how does the number compare with the CCCC maximum of 60?
- How many instructors teach at more than one campus or institution, and how much time does each appointment leave for office hours and early intervention?
- Does the DFW rate in your own data differ by section size or by instructor appointment type?
How do faculty working conditions affect early intervention and response?
The question moves the conversation beyond curriculum and student preparation toward resource allocation: class size, support for faculty, and proactive student services.
Cite this resource
Cole, K., Hassel, H., et al. (2025, revised 2026). Data-Driven Advocacy Guide. The Writing Program Exchange. https://thewritingprogramexchange.org/data.html
For the benchmark figures, cite the source as: Community-sourced national course cap dataset, 435+ institutions, compiled by Holly Hassel and colleagues, via The Writing Program Exchange (2026).