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Record W2620312830 · doi:10.5539/ass.v13n6p74

The Effectiveness of Likes Method in Improving Reading Skills of Orang Asli Students

2017· article· en· W2620312830 on OpenAlexvenueno aff
Norwaliza Abdul Wahab, Ridzuan Jaafar, Ramlee Mustapha, Arasinah Kamis, Norhayati Mohd Affandi

Bibliographic record

VenueAsian Social Science · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Islamic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMalayVowelRemedial educationReading (process)PsychologyConsonantMathematics educationSignificant differenceClass (philosophy)Computer scienceMathematicsLinguisticsSpeech recognitionArtificial intelligenceStatistics

Abstract

fetched live from OpenAlex

This study aims to examine the effectiveness of the LIKES method in teaching KVKV (consonant, vowel, consonant, vowel) syllables in bahasa Melayu (Malay language) among Orang Asli students in primary schools. This research was conducted in two Orang Asli schools consisting of pupils aged 8 to 10 years as the subjects of the study. Quasi-experimental methods were used to determine the effectiveness of the LIKES method. Assessments were carried out for eight weeks including diagnostic tests as well as pre and post tests. The results showed significant differences in the reading skills of the control group (CG) and experimental group (EG). The study found that the reading skills according to gender was not a significant. Results from the observations showed the Orang Asli students are more focused and enjoyed to learn while using LIKES method in class. The findings clearly show that the LIKES method are suitable to be given to Orang Asli students, or students in remedial classes to improve the skills of reading especially KVKV syllables in bahasa Melayu.

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)0.010
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.602
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.019
GPT teacher head0.432
Teacher spread0.412 · 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.

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

Citations5
Published2017
Admission routes1
Has abstractyes

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