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Record W4391064506 · doi:10.5539/jel.v13n2p37

Development of Reading Skills for Thai Grade 1 Students Through Smart Training Application Innovation

2024· article· en· W4391064506 on OpenAlexvenueno aff
Sirisuda Thanavanitchayakul, Patcharee Srichok

Bibliographic record

VenueJournal of Education and Learning · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology-Enhanced Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsReading (process)Mathematics educationTest (biology)Nonprobability samplingPsychologyPedagogyPopulationMedicine

Abstract

fetched live from OpenAlex

This research aimed to create a smart training application innovation to develop Thai language reading skills for Thai grade 1 students, targeting an efficiency standard of 80/80. The study compared the reading abilities of Thai grade 1 students before and after using the application. These students were from the Network School of the Teacher Professional Experience Center, Faculty of Education, Loei Rajabhat University. Specifically, 13 students from Ban Na Si School were selected using purposive random sampling for the first semester of the academic year 2023. This school was intentionally selected due to its eagerness to implement this innovative approach. The study utilized two versions of the smart training application: one focused on words without tone marks and the other on consonant blend words. Four lesson plans were designed (two for each version), and a 20-question reading achievement test was administered. Through the E1/E2 value calculation and a t-test, it was deduced that the application surpassed the 80/80 efficiency standard, scoring 81.15/86.15. Furthermore, students exhibited a significant improvement in their reading skills after using the application, as evidenced by a higher post-study performance at a 0.05 significance level.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.643
Threshold uncertainty score0.311

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.059
GPT teacher head0.442
Teacher spread0.383 · 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 designQualitative
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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