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Record W2119335715 · doi:10.1177/1469787411402483

The relative benefits found for students with and without learning disabilities taking a first-year university preparation course

2011· article· en· W2119335715 on OpenAlexafffund
Maureen J. Reed, Deborah J. Kennett, Tanya Lewis, Eunice Lund‐Lucas

Bibliographic record

VenueActive Learning in Higher Education · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsUniversity of TorontoTrent UniversityToronto Metropolitan University
FundersTrent UniversityFaculty of Arts, Ryerson University
KeywordsPsychologyPsychosocialLearning disabilityInclusion (mineral)Medical educationHigher educationGeneral educationAcademic achievementMathematics educationStudy skillsDevelopmental psychologyMedicineSocial psychology

Abstract

fetched live from OpenAlex

Positive outcomes have been reported for university preparation courses for students without disabilities. Little is known about whether these courses can offer the same benefit to students with learning disabilities and whether the inclusion of psychosocial factors, in addition to academic skills, would benefit both groups. First-level students with and without learning disabilities were tested on variables known to influence academic performance at the beginning and end of a university preparation course. Results revealed that students entering university with and without learning disabilities have similar challenges. Both groups showed increases in attentiveness, and academic and general resourcefulness after the course. Students with learning disabilities experienced greater gains in academic self-efficacy in comparison to their non-disabled peers. The study showed benefits in including psychosocial measures in a university preparation course, and that integrating students with learning disabilities into the course could help to alleviate the limited resources of disabilities programs.

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 score0.054
Threshold uncertainty score0.933

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 studies0.0010.000
Scholarly communication0.0000.001
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 head0.074
GPT teacher head0.375
Teacher spread0.302 · 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

Citations36
Published2011
Admission routes2
Has abstractyes

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