A Model of Online Teacher Professional Development in Chemical Subject of Middle School
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".