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Record W2563291427 · doi:10.4018/ijksr.2016100105

The Impact of Accreditation on Student Learning Outcomes

2016· article· en· W2563291427 on OpenAlex
Tayeb Brahimi, Akila Sarirete, Rania Ibrahim

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Knowledge Society Research · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsToronto Metropolitan University
FundersYükseköğretim KuruluCouncil for Higher Education
KeywordsAccreditationCommissionHigher educationMedical educationProcess (computing)Political sciencePrincipal (computer security)BusinessMedicineComputer science

Abstract

fetched live from OpenAlex

In recent years, student outcomes in higher education has captured the interest and concern of accreditation communities, governments, employers as well as international organizations. Student outcomes is becoming the principal gauge of higher education's effectiveness and accreditation bodies expect learning outcomes to be well defined, articulated, assessed, verified, and used as a guide for future improvement. The paper investigates the impact of accreditation on student outcomes in higher education and examines the impact of two accreditation bodies on student outcomes, namely: The National Commission for Academic Accreditation and Assessment (NCAAA) established by the Higher Council of Education in Saudi Arabia and the Accreditation Board for Engineering and Technology Inc. (ABET). Results from a course in Mathematics at Effat University, Jeddah, KSA, showed how important the learning outcome is in the process of re-evaluating strategies and methodologies used in the learning process.

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.

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)0.019
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.015
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 science0.0010.000
Research integrity0.0000.001
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 head0.280
GPT teacher head0.636
Teacher spread0.356 · 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