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Record W3198512052 · doi:10.5430/ijhe.v11n2p13

The Transition Online: A Mixed-Methods Study of the Impact of COVID-19 on Students with Disabilities in Higher Education

2021· article· en· W3198512052 on OpenAlexaffvenueabout
Laura Mullins, Jennifer Mitchell

Bibliographic record

VenueInternational Journal of Higher Education · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsBrock University
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)PsychologyPandemicGeneral partnershipMedical educationMental healthTransition (genetics)Higher educationAccommodationStudent engagementPedagogyMedicinePolitical scienceDiseasePsychiatry

Abstract

fetched live from OpenAlex

Following the World Health Organization’s announcement of the global pandemic because of the Coronavirus Disease 2019, most Canadian universities transitioned to offering their courses exclusively online. One group affected by this transition was students with disabilities. Previous research has shown that the university experience for students with disabilities differs from those of their non-disabled peers. However, their unique needs are often not taken into consideration. As a result, students can become marginalized and alienated from the online classroom. In partnership with Student Accessibility Services, this research revealed the impact of the transition to online learning because of the pandemic for university students with disabilities. Students registered with Student Accessibility Services completed a survey about the effects of online learning during a pandemic on the students’ lives, education, and instructional and accommodation. It was clear from the results that online education during COVID-19 affected all aspects of the students’ lives, particularly to their mental health. This research provided a much-needed opportunity for students with disabilities to share the factors influencing their educational experience and identified recommendations instructors should consider when developing online courses to increase accessibility and improve engagement.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.119
Threshold uncertainty score0.767

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.000
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 head0.059
GPT teacher head0.509
Teacher spread0.450 · 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

Citations8
Published2021
Admission routes3
Has abstractyes

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