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Record W3187081943 · doi:10.23960/jiip.v2i1.21789

Peningkatkan Kemampuan Berhitung Permulaan Melalui Media Sempoa

2021· article· en· W3187081943 on OpenAlexaff
Erna Olua, Diana Setyaningsih, Yohana B. Pabendan

Bibliographic record

VenueJurnal Inovatif Ilmu Pendidikan · 2021
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsDocumentationAbacus (architecture)Statistical analysisAction researchMathematics educationQualitative analysisPsychologyQualitative propertyMathematicsQualitative researchComputer scienceStatisticsSociologyGeographySocial science

Abstract

fetched live from OpenAlex

The purpose of this study was to describe the increase in numeracy skills of 0-10 through the Abacus Media for Group B children in Integrated PAUD Nagallo Marannu, Puncak Jaya Regency, Academic Year 2017/2018. This research was conducted on group B children, amounting to 16 people. This research is an action research with the method of Kemmis and Taggart which consists of 4 stages, namely (plan, act, observe and reflect). This study consisted of 2 cycles, each of which consisted of 3 meetings. Data collection techniques using observation, field notes, and documentation. Data analysis using quantitative and qualitative. Quantitative data analysis was performed with statistical descriptions to compare pre-cycle to cycle II. Qualitative analysis is carried out by analyzing data from observations, field notes and verified documentation. The results showed that there was an increase in the ability to count numbers 0-10 through the abacus media with a score in the pre-cycle of 46.35% increasing to 66.66% in the first cycle and an increase of 82.29 in the second cycle with the very well developed category.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.037
GPT teacher head0.305
Teacher spread0.267 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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