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Record W3156429910 · doi:10.11591/ijere.v10i2.21173

Students’ attitudes towards physics in Nine Years Basic Education in Rwanda

2021· article· en· W3156429910 on OpenAlexaboutno aff
Agnes Mbonyiryivuze, Lakhan Lal Yadav, Maurice Musasia Amadalo

Bibliographic record

VenueInternational Journal of Evaluation and Research in Education (IJERE) · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsnot available
FundersAfrican Centre of Excellence for Innovative Teaching and Learning Mathematics and Science, University of Rwanda
KeywordsMathematics educationQuarter (Canadian coin)Physics educationAttention spanSpan (engineering)Test (biology)PsychologyEngineeringCognitionGeographyBiology

Abstract

fetched live from OpenAlex

This study investigated students’ attitudes towards physics in Nine Year Basic Education (9YBE) in Rwanda. Data were collected from 380 students from Kayonza and Gasabo Districts using a physics attitudes test. Findings illustrated that more than a quarter of participants felt that learning physics is boring. About 39% think that the subject of physics does not relate to the real-world experience. A significant number of participants had negative attitudes towards physics in terms of the effort required for learning. The findings also showed that the overall level for participants in physics problem-solving skills was low. The item-by-item analysis showed that the differences between responses of students from rural schools and their counterparts from urban schools in categories of problem-solving and physics concepts connections and understanding are statistically significant. It was found that many students in rural schools need to know more about the interpretation of a new equation to be able to apply it to a new physics problem.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.276
GPT teacher head0.621
Teacher spread0.345 · 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 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

Citations38
Published2021
Admission routes1
Has abstractyes

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