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Record W3215107680 · doi:10.5539/ies.v14n12p93

Literature and Project-Based Learning and Learning Outcomes of Young Children

2021· article· en· W3215107680 on OpenAlexvenueno aff
Veena Prachagool

Bibliographic record

VenueInternational Education Studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsnot available
FundersMahasarakham University
KeywordsDebriefingPsychologyFocus groupExperiential learningReading (process)PerceptionCooperative learningVariety (cybernetics)Action researchQualitative researchActive learning (machine learning)PedagogyTeaching methodSocial psychologySociologyComputer science

Abstract

fetched live from OpenAlex

In the early years, children learn by taking an action and touching opportunities which experiences the world as much as possible. It is an internal process that allows children to meaningfully reflect their experiences from abstract to further learning. Literature and project-based learning management is a learning approach that strengthens the attitude of the pursuit of knowledge, helping children to have a habit of reading, creating opportunities to leading in the discovery of something meaningful to life. The research objectives were to study learning outcomes of young children through literature and project-based learning. Twenty-five young children were studied and reported their learning outcomes. Data were collected through variety of methods: observation, debriefing focus group, and interviews after the scenario. Data were collected by qualitative and quantitative methods. The findings indicated that young children had the highest level of understanding and ability to manage literary learning and projects. They also had ability to provide the most literary and project management environment were ranges low and highest based on the different perception and potential of learning. It can be recommended literature and project-based learning is suitable for early childhood education.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.113
Threshold uncertainty score0.278

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.028
GPT teacher head0.381
Teacher spread0.354 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

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