Distances in the Universe: An Inquiry Lab Sequence Taught in West Africa and North America
Bibliographic record
Abstract
Astronomy evokes deep curiosity for many people, making it a beautiful topic for supporting students to learn scientific practices and develop as scientists. We present an inquiry-based lab sequence about distances in the Universe, which we have taught in a first-year astronomy course in Canada and in a summer program for upper-year students in West Africa. Students begin with two warm-up labs where they discover the methods of parallax and the inverse-square law for light to measure distances in their everyday lives. Then they engage in a mini research project in which they ask their own questions about astronomical images, then break down their big questions into smaller questions related to measuring astronomical distances. Students work together in teams to investigate their questions, and finally present their findings to their classmates. Students developing their own questions to investigate is a key scientific practice that is not included in many other inquiry lab curricula. We show evidence that students learned astronomical concepts, had positive feelings about the labs, appreciated the freedom to come up with their own approaches in the labs, and built their self-efficacy as scientists. Since facilitating inquiry is quite different from other kinds of teaching, we describe key features of our facilitation including how we teach new instructors. We describe our curriculum in both Canada and West Africa and offer suggestions for future implementations. We encourage other astronomy instructors to try an inquiry approach to help students develop as scientists while exploring topics they are curious about. Primary Image: Students Rubby Aworka, Vincent Oko Laryea, and Mercy Effah Pardie (left to right) work on designing a way to measure the distance to a model planet in the Distance Inquiry Lab A, at the Pan-African School for Emerging Astronomers (PASEA) 2017 in Accra, Ghana. Image credit: Thai Duy Cuong Nguyen, provided with permission for use in this article under CC-BY-NC 4.0.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".