Proliferation of inscriptions and transformations among preservice science teachers engaged in authentic science
Bibliographic record
Abstract
Abstract Inscriptions are central to the practice of science. Previous studies showed, however, that preservice teachers even those with undergraduate degrees in science, generally do not spontaneously produce inscriptions that economically summarize large amounts of data. This study was designed to investigate the production of inscription while a group of 15 graduate‐level preservice science teachers engaged in a 15‐week course of scientific observation and guided inquiry of two organisms. The course emphasized the production of inscriptions as a way of convincingly supporting claims when the students presented their results. With continuing emphasis on inscriptional representations, we observed a significant increase in the number and type of representations made as the course unfolded. The number of concrete, text‐based inscriptions decreased as the number of graphs, tables and other sorts of complex inscriptions increased. As the students moved from purely observational activities to guided inquiry, they made many more transformations of their data into complex and abstract forms, such as graphs and concept maps. The participants' competencies to cross‐reference ultimate transformations to initial research questions improved slightly. Our study has implications for the traditional methods by which preservice science teachers are taught in their science classes. © 2007 Wiley Periodicals, Inc. J Res Sci Teach 44: 538–564, 2007.
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 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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".