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Record W7111072949 · doi:10.5281/zenodo.17870720

INSTRUCTIONAL STRATEGIES USED BY TEACHERS TO ENHANCE HANDWRITING SKILLS AMONG GRADE 1 LEARNERS

2025· article· W7111072949 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typearticle
Language
FieldSocial Sciences
TopicWriting and Handwriting Education
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsHandwritingClass (philosophy)Qualitative researchQualitative propertyQualitative analysisMotor skill

Abstract

fetched live from OpenAlex

Handwriting remains a foundational skill in early literacy, influencing learners' academic development and fine motor coordination. However, many Grade 1 learners struggle to develop adequate handwriting skills due to various instructional and contextual challenges. This study explores the instructional strategies used by teachers to enhance handwriting skills among Grade 1 learners. A qualitative research approach was employed, using semi-structured interviews to collect data from Foundation Phase teachers in three purposively selected primary schools. The findings revealed that teachers use multi-sensory techniques, fine motor strengthening activities, and guided practice to improve handwriting. However, challenges such as large class sizes, lack of resources, and time constraints were also reported. The study recommends ongoing professional development, provision of adequate resources, and reduced class sizes to support effective handwriting instruction. These insights contribute to improved classroom practices that support early writing development.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.344
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0090.001
Scholarly communication0.0040.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.314
Teacher spread0.294 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2025
Admission routes1
Has abstractyes

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