MétaCan
Menu
Back to cohort

EFFECTIVENESS OF LOW COST MATERIALS ON DIVERSE ACHIEVERS IN THE TEACHING OF PHYSICS AT SECONDARY LEVEL

2019· article· en· W2995687045 on OpenAlexaff
Saifullah Khan, Rehmat Ali Farooq, Nilsa Fleury

Bibliographic record

VenueSir Syed Journal of Education & Social Research (SJESR) · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsScience North
Fundersnot available
KeywordsTest (biology)Mathematics educationSample (material)Government (linguistics)Selection (genetic algorithm)EngineeringComputer sciencePsychologyPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract The equipment’s of science teaching in the most of secondary school is not available in Pakistan. The main purpose of this research is to find the effectiveness of low cost materials on diverse achievers in the physics instructing at secondary level. The nature of the study was experimental. The most suitable design for this experiment was Pre-test, post-test equivalent group design. A sample of 40 pupils was carefully chosen from Government High School No.2 Nowshera Cantt. The experiment continued for six weeks. Independent sample t-test was used for the analysis of data. The group which has been instructed with low cost teaching aids showed successful result scores in the posttest and their achievement level further gets improved. It has been suggested that teachers training institutions ought to build up such a training programs, which would enhance the capacities of teachers in the selection and in the development of apparatuses, using low cost materials for practical work

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.186
GPT teacher head0.516
Teacher spread0.329 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueSir Syed Journal of Education & Social Research (SJESR)Same topicInnovative Teaching MethodsFrench-language works237,207