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A Framework for Characterizing Learning Contributions Across the Initial Achievement Spectrum — arXiv:2609.12753

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.12753
License (as archived): CC0-1.0
Archived: 2026-10-16T04:13:26.699Z
SHA-256 of the archived text: b918def9ba76841691e90ca6aa508c9c879998d812d6317c2414f1f6f597abeb
Stable URL of this page: https://metraipolis.com/documento/dc_cb2de090997d

How to cite

A Framework for Characterizing Learning Contributions Across the Initial Achievement Spectrum — arXiv:2609.12753 — arXiv (en), https://arxiv.org/abs/2609.12753 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_cb2de090997d (Metraipolis archive dc_cb2de090997d, sha256 b918def9ba76841691e90ca6aa508c9c879998d812d6317c2414f1f6f597abeb)

BibTeX

@misc{dccb2de090997d,
  title = {A Framework for Characterizing Learning Contributions Across the Initial Achievement Spectrum — arXiv:2609.12753},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.12753},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_cb2de090997d; sha256 b918def9ba76841691e90ca6aa508c9c879998d812d6317c2414f1f6f597abeb},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.12753. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Conceptual assessments are widely used in physics education research to evaluate changes in student understanding, yet class average measures can obscure how those changes are distributed across students with different levels of initial achievement. We introduce a Learning Contribution Framework that characterizes this distribution through the Learning Contribution Curve (LCC) and Learning Contribution Profile (LCP). For a general contribution measure G, the LCC represents cumulative contribution across students ranked by initial achievement, whereas the LCP describes the local mean contribution relative to the population mean of the individual G values. We apply the framework to the individual Hake normalized gain and develop a statistical model linking LCC and LCP behavior to the joint structure of pretest and posttest scores. Under the central assumption that the conditional mean of the subsequent score is linear in the initial score, we identify a score structure parameter $\beta$ and a critical score structure parameter $\beta_c$. Whether $\beta$ is greater than, less than, or equal to $\beta_c$ determines whether the expected LCP increases, decreases, or remains constant across initial achievement. Given the pretest distribution, the model further yields analytical predictions for the complete LCP and LCC that closely reproduce simulation results. Application to classroom concept-assessment data illustrates how the framework reveals local and cumulative patterns of learning contribution that are not evident from an overall class average revealed by the traditional Hake's normalized gain.

Tema degli esploratori: ricerca e scienza aperta (retro-catalogo arXiv: annunci del 2026-09-14 in physics.ed-ph).
Categorie arXiv: Physics Education (physics.ed-ph).
Pagina del lavoro: https://arxiv.org/abs/2609.12753 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T03:59:12.679Z dalla pagina https://arxiv.org/catchup/physics.ed-ph/2026-09-14?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).