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Record W2918604443 · doi:10.5430/wje.v9n1p172

A Case Study on Preservice Primary School Teachers’ Flower Care at School within the Scope of Community Service Practices Course

2019· article· en· W2918604443 on OpenAlexvenueno aff
Zeynep Doğan

Bibliographic record

VenueWorld Journal of Education · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicDiverse Educational Innovations Studies
Canadian institutionsnot available
Fundersnot available
KeywordsScope (computer science)Data collectionService (business)PsychologySample (material)Medical educationQualitative researchPerspective (graphical)Qualitative propertyMathematics educationPedagogySociologyComputer scienceMedicineSocial science

Abstract

fetched live from OpenAlex

The aim of this study is to present the details of a practice that is carried out within the scope of Community ServicePractices Course from a scientific perspective. And also to investigate the views of the pre-service teachers related tothe topic. Research design is determined as a case study method. For the data collection, interview method was usedfrom qualitative data collection methods. The sample is composed of 8 pre-service primary school teachers. Within therelevant course, a potted flower to be determined by the pre-service teachers was planted in a pot in a suitable place inthe faculty, and the students were given the task of undertaking all kinds of care for the flower during the semester.Semi-structured interviews with the pre-service teachers were held at the end of the term and their opinions were taken.According to the results, all of the pre-service teachers find the implementation useful and important for their teachingcareer. They stated that they got information about flower care, they gained awareness and they were impressedaffectively.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.009
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.003
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.067
GPT teacher head0.335
Teacher spread0.269 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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