Learners given appropriate materials and prompts can successfully develop, articulate, and elaborate rich epistemic ideals for good models and good sources¶

Claim · All claims
Evidence · 1 study · theoretical r3 · q2
Currency · newest study 2024 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Given appropriate materials and prompts, learners can develop and articulate rich epistemic ideals, and students as young as fifth graders can metacognitively justify why these criteria are valuable. → Sarit Barzilai & Clark A. Chinn 2024

Evidence¶

Sarit Barzilai & Clark A. Chinn 2024¶

Sarit Barzilai & Clark A. Chinn. (2024). The AIR and Apt-AIR Frameworks of Epistemic Performance and Growth: Reflections on Educational Theory Development. Educational Psychology Review. https://doi.org/10.1007/s10648-024-09927-5

q2 · i? · theoretical · r3

The review reports findings from the authors' design studies with middle school students developing criteria for evaluating models, arguments, or sources. It further reports that "students as young as fifth graders can not only develop and use productive ideals for good models" but also "metacognitively justify why these criteria are valuable."

"we observed that, given appropriate materials and prompts, learners can successfully develop, articulate, and elaborate rich epistemic ideals for good models (e.g., answers the question, provides visual aids, supported by evidence) or good sources (e.g., expert authors, no vested interests)"

Discussion¶

Related Claims¶

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• Before metacognitive skill training, fifth graders possessed none of the metacognitive skills later trained, so metacognitive skills are task-specific at first and become domain-general only through transfer — related
• Prompting learners to self-explain, one generative strategy, improves learning by a moderate average amount; other generative activities are not tested by the evidence recorded here — related
• Judgement is theorized as the evaluative locus of educational integrity within GenAI-augmented authorship — related