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Record W4380853424 · doi:10.5539/jel.v12n4p128

Participatory Action Learning of Pre-Service Teachers to Develop Learning Materials with Plastic Recycling for Primary School Students in Central Thailand

2023· article· en· W4380853424 on OpenAlexvenueno aff
Yuttana Chaijalearn, Thanin Intharawiset, Phatsayakorn Laosawatdikul, Satapon Moongkumglang, Wijitkon Kumrat

Bibliographic record

VenueJournal of Education and Learning · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Methods and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsParticipatory action researchCitizen journalismFocus groupAction researchPsychologyPedagogyQualitative propertyQualitative researchPresentation (obstetrics)Service (business)Medical educationMathematics educationSociologyMedicinePolitical scienceBusinessComputer scienceSocial scienceMarketing

Abstract

fetched live from OpenAlex

The research aimed 1) to study the need in developing learning materials with plastic recycling for primary school students in Central Thailand, 2) to develop learning materials with plastic recycling for primary school students in Central Thailand based on action learning of pre-service teachers and 3) to distill the lesson learned from developing learning materials with plastic recycling for primary school students in Central Thailand based on participatory action learning of pre-service teachers. Participatory Action Research (PAR) was used. The sample in the research consisted of (1) school administrators, teachers, staff of teachers, and student representatives, accounting for 30 people; 2) experts in learning management, accounting for 9 people; and 3) pre-service teachers, accounting for 100 people, coming with a total of 139 people. The instrument used in the research consisted of 1) unstructured interview forms, 2) assessment forms of learning materials, and 3) recording forms of focus group discussion. Qualitative data was analyzed based on content analysis and the presentation was in the form of descriptive analysis. The study results revealed that: 1) Regarding the need for developing learning materials with plastic recycling for primary school students in Central Thailand, it was found that recycling is suitable for being developed as learning materials for students due to utilizing recycled materials worthily. Moreover, durable learning materials can be built as well and can be used for a longer period of time. This does not increase the budget burden of schools and is consistent with the concept of education for sustainable development. The researchers have synthesized the obtained data to be the participatory action approach for developing learning materials or 4P consisting of (1) Plan, (2) Process, (3) Present and (4) Practice. 2) Regarding developing learning materials with plastic recycling for primary school students in Central Thailand based on action learning of pre-service teachers, it was found that the pre-service teachers were able to design and develop learning materials from plastic recycling creatively and to use them in schooling management efficiently. 3) Regarding distilling the lesson learned from developing materials with plastic recycling for primary school students in Central Thailand based on participatory action learning of pre-service teachers, the competency of being professional teachers important of pre-service teachers in 4 aspects or so-called LISA consisting of (1) Learning Management, (2) Innovation (3) Self-development and (4) Assessment of Learning This helps promote the competency of earning a living of the pre-service teachers to be more efficiently.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.005
Scholarly communication0.0040.003
Open science0.0020.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.086
GPT teacher head0.450
Teacher spread0.364 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2023
Admission routes1
Has abstractyes

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