Why educate “little scientists?” Examining the potential of practice‐based scientific literacy
Bibliographic record
Abstract
Abstract In recent years, a number of curriculum reform projects have championed the notion of having students do science in ways that move beyond hands‐on work with authentic materials and methods, or developing a conceptual grasp of current theories. These reformers have argued that students should come to an understanding of science through doing the discipline and taking a high degree of agency over investigations from start to finish. This stance has occasionally been mocked by its critics as an attempt to create “little scientists”—a mission, it is implied, that is either romantic or without purpose. Here, we make the strong case for a practice‐based scientific literacy, arguing through three related empirical studies that taking the notion of “little scientists” seriously might be more productive in achieving current standards for scientific literacy than continuing to refine ideas and techniques based on the coverage of conceptual content. Study 1 is a classroom case study that illustrates how project‐based instruction can be carried out when teachers develop guidance and support strategies to bootstrap students' participation in forms of inquiry they are still in the process of mastering. Study 2 shows how sustained on‐line work with volunteer scientists appears to influence students' success in formulating credible scientific arguments in written project reports following an authentic genre. Study 3, using data from three suburban high school classes, suggests that involving students in the formulation of research questions and data analysis strategies results in better spontaneous use of empirical data collection and analysis strategies on a transfer task. The study also suggests that failing to involve students in the formulation of research can result in a loss of agency. The implications of these findings for future research and practice are discussed. © 2004 Wiley Periodicals, Inc. J Res Sci Teach 41: 234–266, 2004
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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 | 0.032 | 0.132 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".