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Record W4395673139 · doi:10.5539/hes.v14n2p109

Developing Academic Achievement in Mathematics on Fractions through Active Learning Combined with Skill Practice for Grade 3 Elementary Students

2024· article· en· W4395673139 on OpenAlexvenueno aff
Thitipat Kumta, Songsak Phusee-orn

Bibliographic record

VenueHigher Education Studies · 2024
Typearticle
Languageen
FieldMathematics
TopicMathematics Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationAcademic achievementPsychologyActive learning (machine learning)Teaching methodElementary mathematicsPedagogyComputer science

Abstract

fetched live from OpenAlex

This research aims to 1) develop an effective Active Learning plan combined with skill practice according to the 70/70 criterion, 2) study the learning achievement index through Active Learning combined with skill practice, and 3) investigate the satisfaction towards Active Learning combined with skill practice. The sample group consisted of 27 Grade 3 students from Muang Wapi Pathum School, during the first semester of the 2023 academic year, selected through purposive sampling. The research tools included 1) 10 Active Learning plans totaling 10 hours, 2) mathematics skill practice, 3) an achievement test consisting of 15 multiple-choice questions, and 4) a satisfaction questionnaire regarding Active Learning combined with skill practice, using a 5-point Likert scale with 13 items. The statistical analysis included mean, percentage, standard deviation, and effectiveness index. The research found that the Active Learning plan was effective with an efficiency of 81.00/82.20, which meets the predefined criterion of 70/70. The effectiveness index was 0.6828, indicating that students improved their learning by 68.28 percent. Overall satisfaction was at the highest level, with an average score of 4.54.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.465
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.211
GPT teacher head0.525
Teacher spread0.314 · 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 designTheoretical or conceptual
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

Citations0
Published2024
Admission routes1
Has abstractyes

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