Bibliographic record
Abstract
Teachers are the key element in effective teaching and learning of astronomy. Yet very few teachers have any background in astronomy or astronomy teaching. At the elementary school level, very few teachers have any background in science at all. How much astronomy should teachers know? How should they learn it? This leads to another important issue: many teachers, especially at the elementary level, have science and mathematics “anxiety,” and may transmit this anxiety to their students. It's important for teachers to have and transmit interest and enthusiasm. How can these desiderata be built into pre-service teacher education? In Chapter 10, Mary Kay Hemenway addresses the complex topic of pre-service teacher education. Like the curriculum, teacher education varies greatly from one country to another, and even within a single country. There are two models of teacher education: concurrent and sequential. In the concurrent model, teachers receive their content courses and pedagogy courses concurrently. The advantage is a greater integration of content and practice. In the sequential model, teachers receive a regular undergraduate degree along with hundreds of other students who are generally not prospective teachers. It may be very frustrating for prospective teachers to take science courses that are taught by the traditional lecture, textbook, and regurgitation exam method, and then to learn in teachers' college that this is not a very effective approach and that, further, this method is rarely used in schoolteaching! Of course, one of the great anomalies of the education system is that college and university instructors seldom receive any pre-service or in-service training in teaching and learning.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.413 | 0.210 |
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".