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Record W3015923152 · doi:10.21083/ajote.v9i0.5413

Higher Grading Standards and Academic Motivation: Perceptions of Students and Teachers in a Lagos (Nigeria) Private Secondary School

2020· article· en· W3015923152 on OpenAlexvenueno aff
Innocent Uche Anazia

Bibliographic record

VenueAfrican Journal of Teacher Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Assessment and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsGrading (engineering)Thematic analysisMathematics educationPsychologyPerceptionQualitative researchMedical educationPedagogySociologyMedicineEngineeringSocial science

Abstract

fetched live from OpenAlex

Abstract This study used qualitative research method to investigate the views of students and teachers of a private secondary school in Lagos State, Nigeria on whether higher grading standards motivate students. The study was prompted by the decision of the management of the school to increase its grading standard. To guide the study, 10 students and 7 teachers participated in the study. Data were generated using interview technique which centred on three objectives or themes of the study. However, the second objective of the study was targeted at the students, while the third objective or theme was targeted at the teachers. Thematic analysis was used to analyze the perceptions of the students and teachers. The findings revealed that higher grading standards motivate students to study harder and that higher standards benefit both high-achievers and low-achievers. Considering that the study was the first attempt to investigate the issue as it concerns Nigeria, suggestions were made on future studies.

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)0.002
metaresearch head score (Gemma)0.003
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.419
Teacher spread0.360 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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