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Record W4383681795 · doi:10.5430/ijhe.v12n4p15

Faculty Perceptions on the Effectiveness of the Internal Quality Process and its Role on the Teaching and Learning

2023· article· en· W4383681795 on OpenAlexvenueno aff
Khalid Salim Saif Al Jardani

Bibliographic record

VenueInternational Journal of Higher Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsnot available
Fundersnot available
KeywordsQuality assuranceProcess (computing)Quality (philosophy)PerceptionMedical educationPerspective (graphical)PsychologyKnowledge managementComputer scienceEngineeringMedicineOperations management

Abstract

fetched live from OpenAlex

The paper examines the perspective of academics on the impact of internal quality assurance system in the A’Sharqiyah University (ASU) on the teaching and learning process. This internal system is called the Program Annual Monitoring and Review Process (PAMRP). This study covers the academic staff’s awareness of the quality system utilized within the university and their perceptions towards its effectiveness on teaching and students’ learning process. The study highlights the key aspects covered by participants to improve the internal quality assurance process.The study concludes that faculty are almost aware of the quality process and agreed on its positive impact on teaching and learning. They only emphasized the need to provide the part time faculty with enough input like the awareness sessions conducted for the full times.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0040.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.030
GPT teacher head0.417
Teacher spread0.387 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations4
Published2023
Admission routes1
Has abstractyes

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