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Record W4382500748 · doi:10.3389/feduc.2023.1170454

Accessibility in assessment for learning: sharing criteria for success

2023· article· en· W4382500748 on OpenAlexaff
Jill Willis, Julie Arnold, Christopher DeLuca

Bibliographic record

VenueFrontiers in Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsQueen's University
FundersAustralian Research CouncilAustralian Government
KeywordsRubricMathematics educationCurriculumPsychologyDilemmaIntervention (counseling)Best practiceComputer sciencePedagogy

Abstract

fetched live from OpenAlex

Assessment for learning (AfL) practices in secondary schools are intended to help learners understand what expert performances in disciplines look like, and then apply this understanding to their own learning and assessment performances. Common AfL practices such as sharing criteria for success through rubrics and students using them to interrogate exemplars and give feedback rely heavily on the students’ language and attention. Students need to understand and draw on conceptual and collaborative language, and to make connections across several activity stages. Consequently, students with language and/or attentional difficulties and their teachers face a dilemma. On the one hand, AfL practices can provide access to developmentally appropriate curriculum. On the other, AfL practices may present additional barriers to learning. This article identifies some of the barriers students with language and/or attentional difficulties may encounter in common AfL practices, and how teachers adapted sharing of success criteria to design for greater accessibility. Access to learning is conceptualized by referring to Dewey’s principles of continuity and interaction. Interviews with 20 teachers were analyzed to find out how they adapted AfL to be more accessible in an 8 week AfL pedagogical intervention focused on success criteria. Ideas for designing accessible AfL practices from the outset are outlined as teachers realized the role of their language, small steps, visual tools, and regular opportunities for connection and interactions in making it more likely for students to benefit from AfL practices. Given that students with language and/or attentional difficulties represent some of the highest occurrences of disability in student populations, these ideas have immediate relevance for teachers and those who support AfL practices in educational policy and research.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score0.320

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.066
GPT teacher head0.469
Teacher spread0.403 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2023
Admission routes1
Has abstractyes

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