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Record W4295135504 · doi:10.46229/elia.v2i2.513

MATHEMATICAL LITERACY PROFILE OF ELEMENTARY SCHOOL STUDENTS IN INDONESIA: A SCOPING REVIEW

2022· review· en· W4295135504 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Education Learning and Innovation (ELIa) · 2022
Typereview
Languageen
FieldMathematics
TopicMathematics Education and Pedagogy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMathematics educationLiteracyProcess (computing)Information literacyComputer sciencePsychologyPedagogy

Abstract

fetched live from OpenAlex

Mathematical literacy becomed one of skills that had to be mastered by the students in this era. Mathematical literacy could start to be learned in elementary level. This study aimed to describe the research trends of mathematical literacy profile for elementary school students in Indonesia. This study used scoping review research with 5 steps including 1) identifying the initial research questions; 2) identifying relevant studies; 3) study selection; 4) charting and collating the data; and 5) summarizing and reporting the results. The data exploration process was taken through open-access websites such as Google Scholar, ERIC, and Springer using keywords “Literasi Matematika Siswa Sekolah Dasar”, “Mathematical Literacy of Elementary School Students in Indonesia”. The exploration process was also limited publication for 5 last years. The data reduction process was analyzed using the Preferred Reporting of Items for Systematic Review and Meta-Analyses (PRISMA). The study results were classified to the 3 components such as 1) research methodologies trends, 2) mathematical literacy development, and 3) student’s achievement based on mathematical literacy.

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.

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)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.532
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.146
GPT teacher head0.522
Teacher spread0.376 · 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