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Record W2129434549 · doi:10.5539/cis.v3n4p72

An Introductory of Mental Arithmetic Using Interactive Multimedia for Pre-School Children

2010· article· en· W2129434549 on OpenAlexvenueno aff
Siti Zulaiha Ahmad, Arifah Fasha Rosmani, Mohammad Hafiz Ismail, Suraya Mohammad Shakeri

Bibliographic record

VenueComputer and Information Science · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Technology Integration
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceMental arithmeticInteractive mediaMultimediaMathematics educationTest (biology)ArithmeticAttention spanCognitionPsychologyMathematics

Abstract

fetched live from OpenAlex

Arithmetic is an important skill to learn at an early age. However, teaching arithmetic to pre-school children can be challenging as children around that age have short attention span and prefer to engage in interactive activities. Thus, the purpose of this study is to incorporate multimedia elements into mathematical learning to make it more interactive and exciting to the children. The contents have been specifically designed to visualize the introduction of numbers and to encourage children’s interaction by drawing their attention to the courseware. The result revealed that the students have shown significant improvement over the mental arithmetic approach after being subjected to a typical classroom test. This concludes that the mental arithmetic using interactive multimedia could enhance learning experience for pre-school children.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0050.001

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 head0.010
GPT teacher head0.340
Teacher spread0.330 · 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 designBench or experimental
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

Citations4
Published2010
Admission routes1
Has abstractyes

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