PD Days Under the Moon: Teaching Lunar Phases to In-Service Teachers by Doing Astronomy Like Astronomers Do and its Impact on Their Students’ Learning
Bibliographic record
Abstract
Several school curricula urge K-12 teachers to engage their students in scientific inquiry activities that not only promote students’ learning in science, but also foster students’ understanding of science methodology. Unfortunately, recent large-scale studies have shown that inquiry-based science teaching in school is the exception, rather than the norm. This is especially true for astronomy, which teachers often consider too abstract and remote for inquiry-based teaching. To promote inquiry-based teaching in astronomy, we present an epistemological and historical analysis of the way astronomers build new knowledge and propose to teach astronomy through a scientific inquiry process consisting of “Doing astronomy like astronomers do”. This inquiry-based approach, which also includes observation, modelling, and communication with peers, emulates the different steps astronomers and scientists go through to do empirical science (question, hypothesis, observation, analysis/synthesis, modelling, prediction/application, and communication), transposed into a teaching and learning lesson plan about the phases of the Moon. The crucial steps of observation, analysis/synthesis, and modelling, where astronomers create models as proxies of astronomical objects that cannot be manipulated, is highlighted. This inquiry-based astronomy training, which also promotes conceptual change about lunar phases, was tested with 18 in-service elementary and high school teachers engaged in a professional development (PD) training program. Three participant teachers also taught lunar phases to their own elementary and high school students (N = 104) using the same approach. We present the results of a quasi-experimental study of the impacts of this PD training about lunar phases on the learning gains and self-efficacy of the participating in-service teachers, as well as on their students’ learning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".