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Record W3210690908 · doi:10.5539/jel.v10n6p68

A Model of Online Teacher Professional Development in Chemical Subject of Middle School

2021· article· en· W3210690908 on OpenAlexvenueno aff
Jinglei Wang, Shengquan Yu

Bibliographic record

VenueJournal of Education and Learning · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology-Enhanced Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsProfessional developmentThe InternetPsychologyMathematics educationPedagogyAction researchFaculty developmentSubject (documents)Medical educationMedicineLibrary scienceComputer science

Abstract

fetched live from OpenAlex

With the continuous development of technology and people’s dependence on the Internet, more and more teachers’ professional development is carried out on the Internet. Since no one has studied the professional development of online middle school teachers, we did this study based on this research gap. This article is a research on the professional development of chemistry teachers based on the Internet. It uses content analysis methods to identify research questions and select keywords related to the topic, such as online teacher professional development, chemistry teacher professional development, secondary school teacher professional development and so on and codes the data. From this, we found that the strategies for the professional development of online teachers in middle school chemistry subjects includes chemical content, practice, guidance, exploratory experiment, online communication community, self-reflection, sustainability, continuity and action these eight parts. And online chemical TPD for student outcomes in middle school contains: gain knowledge, skills and confidence, determined their goals and plans, participate in scientific inquiry teaching and improve student performance, promote the transformation of students’ scientific knowledge concepts, and have a positive influence on students’ attitudes towards science.

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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.325
Threshold uncertainty score0.648

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.058
GPT teacher head0.380
Teacher spread0.321 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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