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Record W4288905966 · doi:10.53469/jerp.2022.04(07).01

The Effect of Textbooks on Eliminating Reflection Errors and Number Omission Errors in Early Childhood Teaching

2022· article· en· W4288905966 on OpenAlexaff
Mayur Dangle, G. C. Joshi, Ded -Education P Hd

Bibliographic record

VenueJournal of Educational Research and Policies · 2022
Typearticle
Languageen
FieldComputer Science
TopicEducational Research and Pedagogy
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsCopyingPsychologyReflection (computer programming)PopulationMathematics educationMedicineComputer science

Abstract

fetched live from OpenAlex

recorded 76.5% and 72.5% respectively. There was a 4% improvement. The pretest and posttest of the Experimental group in reflection errors recorded 73.5% and 16.5%. There was 57% improvement. The pretest and posttest for number omission errors in control group showed 68.5% errors and 64.5% respectively. There was a 4% improvement. The pretest and posttest for the Experimental group in number omission errors recorded 64.5% and 21.5%. There was 43% improvement. The study recommended use of adequate and age appropriate materials during instruction. Children should be discouraged from copying other children's work in order to avoid transferring mistakes to their 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 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)0.005
metaresearch head score (Gemma)0.003
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 score0.479
Threshold uncertainty score0.804

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.059
GPT teacher head0.450
Teacher spread0.391 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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