A contemporary review of K–16 astronomy education research
Bibliographic record
Abstract
: Despite astronomy's widespread inclusion in curricula prior to the twentieth century, educational research in astronomy is a relatively new endeavor. As the field of astronomy education research grows, many may find it useful to know what has been done so far. Starting with and expanding beyond the SABER database, a systematic review and classification of the K-12 and higher education literature was performed. Some of the research themes that emerged include: student beliefs and misconceptions; collaborative learning; the large lecture classroom; and education in planetariums. Key studies in these areas are described and a bibliography is presented. Astronomy education research (AER) uses the systematic techniques honed in science education and physics education research to understand what and how students learn about astronomy, and determine how instructors can create more productive learning environments for their students. As the field of AER grows - and it is doing so vigorously - many readers may find it useful to have a concise summary of what has been published to date. The purpose of this paper is to summarize and categorize the various research projects in astronomy education in order to set the stage for subsequent efforts. In researching the field, a number of information sources were consulted. Three electronic resources served as the starting point for the review: the SABER (Searchable Annotated Bibliography of Education Research) database, the American Astronomical Society's education bibliography (http://www.aas.org/education/biblio list.html), and the Astronomical Society of the Pacific's education bibliography (Fraknoi, 1998). Further references were found in a variety of journals, often through a “snowballing” technique of looking through an article's references for new articles.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.014 | 0.031 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".