Patterns vs. Causes and Surveys vs. Experiments: Teaching Scientific Thinking
Bibliographic record
Abstract
The scientific method is a core element of all science. Yet, its different implementations are remarkably diverse, based on the varied concepts and protocols required in each specific instance of science. For experienced scientists, coping with this diversity is second nature: they readily and continually ask tractable questions even outside their expertise, and find the process of forming hypotheses, designing tests, and interpreting results fairly transparent. At the secondary school stage, the scientific method is often introduced as a series of clear steps in a pre-planned lab activity. In between these two stages comes the essential step of abandoning the supports of a step-by-step approach, and instead learning how to work through the scientific method to generate and answer one's own questions. In our experience, this process is rarely taught explicitly. Yet, undergraduate students (even strong students) can have difficulty translating their initial questions into testable hypotheses, and designing and interpreting appropriate corresponding tests. To combat this difficulty, we have developed a conceptual framework that distinguishes the fundamental concepts of pattern and cause. This framework guides undergraduates directly to posing tractable questions, formulating testable hypotheses (descriptive or mechanistic), and designing clear tests (surveys or experiments). Anecdotal evidence, including our in-course assessments and student feedback, suggests this approach leads to improvement of students’ scientific abilities. The benefits are noticeable when students apply the scientific method to their own questions and also while interpreting science reported in biological literature.
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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 | 0.044 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".