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Record W3163393008 · doi:10.19173/irrodl.v22i1.5110

A Scoping Review on Open Educational Resources to Support Interactions of Learners with Disabilities

2021· review· en· W3163393008 on OpenAlexvenueno aff
Jewoong Moon, Yujin Park

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2021
Typereview
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsnot available
Fundersnot available
KeywordsOpen educational resourcesOpen educationEducational technologyComputer scienceKnowledge managementPsychologyMathematics educationWorld Wide Web

Abstract

fetched live from OpenAlex

This scoping review explored the trends in open educational resources (OER) that support the interactions of learners with disabilities and the challenges of supporting these interactions in such environments. Emerging OER and open educational practices allow learners to interact with digital learning resources in self-regulated learning. Since OER assume learners’ self-regulation, research has explored how to promote learner interactions to facilitate better engagement and motivation. Emerging research on OER-enabled pedagogy corroborate this trend. However, despite increasing interest in OER and open educational practices, few studies have demonstrated how OER support various types of interactions for learners with disabilities. Learners with disabilities are likely to experience challenges in interacting with OER due to their modality constraints. A comprehensive literature synthesis is essential to investigate the needs of learners with disabilities in their interactions in OER. In this study, we reviewed and synthesized existing research on how OER and open educational practices support the interactions of learners with disabilities across different OER platforms. Our findings suggest both research and design implications for future OER designs suited for learners with disabilities.

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)0.008
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.697
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0050.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.209
GPT teacher head0.537
Teacher spread0.328 · 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.

Study designSystematic review
Domainnot available
GenreReview

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

Citations14
Published2021
Admission routes1
Has abstractyes

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