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Record W2768083624 · doi:10.5539/res.v9n4p68

The Analysis of the Science Textbooks for the First Three Grades in the Brimary Education in Jordan in the Domain of Science Process Skills (2017)

2017· article· en· W2768083624 on OpenAlexvenueno aff
Mohammad Nayef Mohammad Alayasrah, Shima’ Mkhymr Salih Yahyaa

Bibliographic record

VenueReview of European Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Practices and Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationProcess (computing)PopulationScience educationPsychologyComputer scienceSociology

Abstract

fetched live from OpenAlex

The current study aims at detecting the essential and integrated science process skills in the first three grades of the primary education in Jordan using the analytical and descriptive Method. The study population consists of the science textbooks of the first three grades in the primary education in Jordan in 2015/2016.The sample of the study is its population. The study has shown that the most basic science process skills included in the science textbooks is the observation process. The textbooks also include all the integrated science process skills, and the experimental process is the most frequent one. However, the books do not include the processes of using time and place elations and the connection process. According to its results, the study has recommended the following. The process of using numbers in the science textbooks of the first grade, and also the processes of using time and place processes, and connections process in the first three grades science books along with the basic science process skills in an organized and balanced form, in the first three grades should be included, it also suggests conducting a study about the evaluation of the first three grades teachers, and the changes in the science textbooks of the first three grades in Jordan.

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)0.008
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 score0.006
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.004
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.093
GPT teacher head0.460
Teacher spread0.367 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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