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Record W2802067306 · doi:10.5539/ass.v14n5p1

Enhancing Pre-service Science Teachers’ Understanding and Practices of SocioScientific Issues (SSIs)-Based Teaching via an Online Mentoring Program

2018· article· en· W2802067306 on OpenAlexvenueno aff
Sasithep Pitiporntapin, Naruemon Yutakom, Troy D. Sadler, Lisa M. Hines

Bibliographic record

VenueAsian Social Science · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsScientific literacyBest practiceScience educationPsychologyMathematics educationTeaching methodPedagogyTeacher educationProfessional developmentMedical educationPolitical scienceMedicine

Abstract

fetched live from OpenAlex

Science education reformists in Thailand promote the use of socioscientific issues (SSIs)-based teaching to enrich scientific literacy for global citizenship. To achieve this goal, Thai pre-service science teachers (PSTs) must know how to effectively integrate SSIs into their science teaching practices. The purpose of this study was to enhance PSTs’ understanding and practices of SSIs-based teaching via the online mentoring (OM) program. Three PSTs were selected as case studies, and data were collected from online observations, semi-structured interviews, online discussions, and online document reviews. The analytical methods included within-case and cross-case analysis. This study found that the OM program was effective in enhancing PSTs’ understanding and practices of SSIs-based teaching. As a result, their teaching practices evolved from conveying content knowledge to promoting higher-order cognitive practices. In addition, the PSTs demonstrated a deeper appreciation for OM programs as a means to enhance teaching practices. This research demonstrates how the implementation of OM programs has the potential to be powerful tool for professional development of science educators, which is essential for transforming science educational practices.

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)0.013
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.154
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0100.011
Scholarly communication0.0010.004
Open science0.0010.000
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 head0.174
GPT teacher head0.503
Teacher spread0.328 · 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 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

Citations3
Published2018
Admission routes1
Has abstractyes

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