⏤ 🟡 DRAFT – EARLY WORK IN PROGRESS 🟡 ⏤
Summary: Learning is not linear — it flourishes through cycles of revision, reflection, and feedback that are timely, dialogic, and equity-centered.
Description: Traditional grading often treats learning as a one-shot performance: submit, score, move on. This “one-and-done” model misunderstands how people actually learn and deprives students of the chance to engage meaningfully and grow through actionable feedback. In the GOAL framework, assessment is understood as iterative: a cycle of returning, reworking, and refining.
Iteration does not mean “more grading” for instructors, but rather different structures — scaffolded drafts, resubmissions, revision reflections, peer review, and checkpoints that let students apply feedback to future work. Students come to see their work as dynamic and developmental, not performative and disposable. Importantly, when feedback is timely, specific, and aligned with goals and future learning, it becomes a powerful form of instruction in itself.
Equity requires iteration, too. Carillo (2021) critiques labor-based contracts for assuming steady, normative productivity. Dolmage (2017) shows that retrofitting access fails when structures don’t account for different paces of learning. Price (2011) and Wood (2017) remind us that flexible, nonlinear pacing that anticipates difference is central to mental health, neurodivergence, and disability justice. Iterative practices operationalize this principle, embedding flexibility as a design feature rather than an accommodation after the fact.
Core Practices
- Allow multiple attempts, drafts, or resubmissions, always with opportunities to apply feedback between rounds.
- Make feedback actionable, specific, and tied to learning goals and future tasks — not just correctness or point deductions.
- Use peer review, self-assessment, and reflective dialogue so students learn to interpret and act on feedback, not just receive it.
- Design assignments that highlight progress over time (revision notes, metacognitive checkpoints, “personal best” measures).
- Embed iteration and flexible pacing into course timelines so learning is not tied to a single rigid moment.
Reflective Questions
- Do my students have structured opportunities to learn from and act on feedback, not just receive it once?
- Does my course design assume learning happens on the first try — or does it anticipate multiple pathways and timelines?
- How do I balance workload with responsiveness, so that my feedback is timely, sustainable, and equitable?
- In what ways does iteration serve equity by reducing pressure on higher-stakes, one-shot performance?
Lineage & Influences
- Hattie & Timperley (2007) highlight the centrality of formative feedback in deep learning.
- Elbow (1998) advocates for minimal grading and iterative feedback cycles.
- Stommel (2017) and the ungrading community emphasize revision as essential to real learning.
- Carillo (2021) critiques labor-based grading’s hidden assumptions of steady productivity, underscoring the need for anticipatory design.
- Dolmage (2017) warns against retrofitting, pressing for equity-centered pacing.
- Price (2011) and Wood (2017) introduce and extend flexible, nonlinear pacing that anticipates difference rather than retrofitting accommodation.
- CAST’s UDL framework (2018) reinforces multiple means of engagement and expression, aligning with iterative and flexible practices.
- STEM applications: mastery- and specifications-grading systems create equitable space for ongoing learning (Hackerson et al., 2024; Hartman & Eichler, 2024).