Student Models¶

Strategy · All strategies
Evidence · 3 claims (2 for, 1 mixed) · 10 studies (4 causal, 3 review, 2 quant-synthesis, 1 theoretical), q1–q4 · 1 of 10 report an effect size

Description¶

Student models are authentic examples of peer work — essays, lab reports, solutions, designs — that instructors curate and share so learners can see what the target performance actually looks like at various quality levels. Unlike expert demonstrations, they show work produced by someone at (or near) the learners' own level, making quality criteria concrete and attainable. They are typically used before or during a writing or production task, often alongside rubrics or Assessment criteria.

Design Implications¶

Student models make abstract quality criteria concrete: learners judge their own drafts against a visible peer example rather than a vague standard, which supports self-assessment and revision [~S]. Their effectiveness depends on how they are used — models paired with explicit criteria or structured comparison activities outperform models presented alone [~M]. Including multiple models of varying quality is more effective than a single exemplar, because contrasting cases help learners abstract the underlying criteria rather than imitating surface features Multiple contrasting cases support abstraction of deep structure. [+M]. Flawed or weak models can be especially valuable: analyzing erroneous examples builds conceptual understanding of why something fails Erroneous examples build conceptual knowledge. [+M].

Context¶

Requirements¶

• Authentic, anonymized student work with appropriate permissions
• Explicit quality criteria (rubrics or checklists) to direct attention to relevant features
• A structured comparison or critique activity — learners should evaluate the models, not just read them
• A follow-on task where learners apply the criteria to their own work (Revision, Practice)

Constraints¶

• A single strong model can anchor learners to imitating surface features (topic, style, structure) rather than understanding criteria [-M]
• Models without accompanying criteria or critique prompts produce little improvement; unstructured exposure is largely inert [-M]
• Learners with strong prior knowledge may gain little and can find exemplar study redundant [~M]
• If models are too polished or too distant from learners' current level, they can demotivate rather than inform [-W]

Implementation Variability¶

• Single exemplar + annotation: instructor annotates one model, highlighting how it meets criteria
• Contrasting pair (strong/weak): learners diagnose weaknesses in a flawed model before revising their own
• Progressive set: a sequence of drafts showing revision from weak to strong, making improvement processes visible
• Peer-generated gallery: current students share drafts for structured peer critique, connecting to Peer Review

Target Learners¶

• Novices in a genre or task type who cannot yet recognize quality features on their own [+M]
• Middle-performing students who benefit from seeing attainable peer-level work rather than expert output [~M]
• Less beneficial for advanced learners, consistent with the expertise-reversal pattern Expertise-reversal effect. [~M]

Target Learning Goals¶

• Genre and task understanding: what a competent product looks like in this domain
• Self-assessment and evaluative judgment: internalizing criteria for quality work
• Revision skill: diagnosing weaknesses in drafts, including via flawed models Erroneous examples build conceptual knowledge. [+M]

Instructions¶

1. Select 2–4 anonymized models spanning a quality range; obtain student consent.
2. Attach explicit criteria or a rubric so learners know what to look for.
3. Have learners critique the models against the criteria — identify strengths, weaknesses, and what distinguishes the strongest from the weakest Multiple contrasting cases support abstraction of deep structure. [+M].
4. Debrief as a class to consolidate the criteria into shared language.
5. Require learners to apply the same criteria to their own draft and revise (Assessment for Learning).

Related Strategies¶

• Worked Examples — the expert-produced counterpart; student models trade expert polish for attainability and evaluative-judgment practice
• Peer Review — critiquing student models rehearses the same evaluative skills peer review requires
• Rubrics — criteria that make model comparison productive rather than superficial

Examples¶

• Calibrated Peer Review — learners first review curated sample essays and are "calibrated" against instructor scoring before reviewing peers, embedding student models directly into the assessment loop.
• Writing-across-the-curriculum programs (e.g., Michigan State's Writing Center exemplar collections) publish annotated student essays by genre so students can compare their drafts to authentic peer work.
• UK "exemplar-based assessment" practice in A-level coursework — teachers share anonymized past-student scripts with exam-board commentary to build shared evaluative judgment.

Key Sources¶

• Sadler, D. R. (1989). Formative assessment and the design of instructional systems. Instructional Science, 18(2), 119–144. doi:10.1007/BF00117714
• Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in Education: Principles, Policy & Practice, 5(1), 7–74. doi:10.1080/0969595980050102
• Nicol, D. J., & Macfarlane-Dick, D. (2006). Formative assessment and self-regulated learning: A model and seven principles of good feedback practice. Studies in Higher Education, 31(2), 199–218. doi:10.1080/03075070600572090
• Panadero, E., & Jonsson, A. (2013). The use of scoring rubrics for formative assessment purposes revisited: A review. Educational Research Review, 9, 129–144. doi:10.1016/j.edurev.2013.01.002
• Topping, K. J. (2009). Peer assessment. Theory Into Practice, 48(1), 20–27. doi:10.1080/00405840802577569