MétaCan
Menu
Back to cohort
Record W2341148060 · doi:10.5539/jel.v5n3p40

Magic Finger Teaching Method in Learning Multiplication Facts among Deaf Students

2016· article· en· W2341148060 on OpenAlexvenueno aff
Liong Kon Thai, Mohd Hanafi Mohd Yasin

Bibliographic record

VenueJournal of Education and Learning · 2016
Typearticle
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationPsychologyMultiplication (music)RespondentTest (biology)PerceptionMAGIC (telescope)Context (archaeology)Mathematics

Abstract

fetched live from OpenAlex

<p>Deaf students face problems in mastering multiplication facts. This study aims to identify the effectiveness of Magic Finger Teaching Method (MFTM) and students’ perception towards MFTM. The research employs a quasi experimental with non-equivalent pre-test and post-test control group design. Pre-test, post-test and questionnaires were used. As many as 70 deaf students from three special education primary schools in Selangor and Federal Territory were gathered as research respondent. Data were analyzed by using descriptive and inferential statistics of t-test. Findings from the t-test analysis showed that MFTM has a significant effect on multiplication facts achievement among deaf students whereas conventional teaching method does not given a significant effect on multiplication facts achievement among them. The findings from questionnaires found that the deaf students have high level of perception towards MFTM in the dimensions of interest, self-confidence, persistence and motivation in learning multiplication facts. The findings serves as an implication towards students, parents, teachers, Special Education Division and Malaysia Education Ministry in terms of awareness, involvement, planning and implementation in the context of diversifying of multiplication facts teaching method, and the suitability of supporting materials in teaching and learning multiplication facts.<strong></strong></p>

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)0.003
metaresearch head score (Gemma)0.001
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 score0.270
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 integrity0.0000.001
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 head0.030
GPT teacher head0.422
Teacher spread0.392 · 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

Citations16
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Education and LearningSame topicHearing Impairment and CommunicationFrench-language works237,207