Bibliographic record
Abstract
In this study, working science teachers’ online behaviors were examined for the knowledge the teachers obtained from the Internet and how this knowledge affects classroom practice. TPACK, a construct by Mishra & Koehler, suggests that teachers possess different forms of professional knowledge that can be engaged during their practice. This knowledge can arguably also be represented and displayed online and digitally. A framework based on TPACK was utilized to categorize websites for the professional knowledge they represented. Five science teachers participated in a study of their online web resources. Over 1500 Internet web logs from participating science teachers were gathered and analyzed using this TPACK framework. In addition to web logs, data collection for this study included a questionnaire and over 300 minutes of semi-structured interviews about how the TPACK obtained from the Internet affected teachers’ everyday practice. The findings revealed that science teachers obtain certain types of TPACK knowledge online, such as CK and PCK, more than others from the Internet. Science teachers reported that the Internet extended their own knowledge of science (CK) and how to teach it (PCK) and helped them to enhance student engagement with science content. This study contributes to a broader understanding of self-directed forms of professional development by teachers that increasingly tends to occur on the Internet and the value of the TPACK framework for examining knowledge online.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.010 |
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".