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Record W4226190183 · doi:10.1504/ijlc.2022.119506

Education quality comparing between official measurement scale and inter-counterparts' perception: a new horizon for learning assessment

2021· article· en· W4226190183 on OpenAlexaff
Gazi Mahabubul Alam, Md. Abdur Rahman Forhad

Bibliographic record

VenueInternational Journal of Learning and Change · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsDalhousie University
Fundersnot available
KeywordsHorizonScale (ratio)PerceptionQuality (philosophy)PsychologyItem response theoryMathematics educationComputer sciencePsychometricsGeographyMathematicsCartography

Abstract

fetched live from OpenAlex

Referring to the official measurement scale, global evidences confirm that 'education quality' has continuously been improving. On the other hand, studies examining the role of education discover that education continuously fails to play the desired role. Therefore, claims are made that 'education quality' is denting. Keeping this view in mind, this research is conducted to assess the education quality, making a comparison between the official measurement scale and 'inter-counterparts' perception'. Both primary and secondary data are used. According to the official measurement scale, 'education quality' has been improved substantially for every provision-primary to tertiary. Apparently, the entire system functions well where performances and 'education quality' of all provisions are synchronised. On the other hand, 'inter-counterparts' perception' indicates that 'education quality' of all provisions deteriorates gradually and they live in an isolation. This research suggested a new horizon for assessment which would ensure substantial learning outcomes; helping the developing world.

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 score0.214
Threshold uncertainty score0.345

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 science0.0000.000
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 head0.142
GPT teacher head0.448
Teacher spread0.306 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

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