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Record W2807436251 · doi:10.5539/jel.v7n3p259

Uses of Mathematics Textbooks for Grade (4-8) as Per Basic Concepts and Questions Levels in TIMSS Test: A Study Conducted in Kingdom of Saudi Arabia Schools

2018· article· en· W2807436251 on OpenAlexvenueno aff
Abdul Tamimi

Bibliographic record

VenueJournal of Education and Learning · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Islamic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationTest (biology)CurriculumMathematicsPopulationPsychologyPedagogyMedicine

Abstract

fetched live from OpenAlex

This study tried to explore the degree of representation of math textbooks for grades (4-8) in the Kingdom of Saudi Arabia concerning the key concepts, shape, and levels of questions used in the TIMSS test. The study population of this study includes both students and teachers from fourth grade to eighth grade. The goal of this study was associated with six key concepts including numbers and their operations, algebra, geometry, measurement, statistics and probability, and a pro-rata. The researcher analyzed the questions and exercises used in the math textbooks to identify their effectiveness and efficiency. In addition, the researcher also calculated the percentages, the levels, and the shape for each key concept. The results of the study were organized in frequency tables. In the light of those results, the researcher recommended the need to rewrite the mathematics curriculum and textbooks for grades (4-8) to focus on the level of questions and exercises used to be best fit for the students comprehending level. Furthermore, the researcher also recommended to developed questions in the form of multiple-choice focusing on the content of the main concepts (statistics and probabilities, and a pro-rata) because of its importance in the life of the student. In addition, the resented figured out the need of conducting similar studies on the analysis of the results for Saudi students in an international math test, which was conducted in 2011 and 2015.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.148
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.093
GPT teacher head0.435
Teacher spread0.342 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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