Developing scientific literacy of grade five students : a teacher-researcher collaborative effort
Bibliographic record
Abstract
Collahorøive EforlThe goal of this study was to deveþ scieúific literacy of grade five studenfs in the context of a curricular unit on Weather.To fulfill this goal, a teacher-researcher team used the C.ommon Knowledge C.onstructionModel (CKC.M), designed by Ebenezer and Connor (1998), and engaged themselves in a collaborative journey to develop three notions of scientific literacy--the "$'hd," the 'tow," and the 'lvhy'' of science.This was an ethnographic, classroom based study, in which the researcher was a pmticipant observer of all school endeavors.Tlre data were collected by means of classroom observations, interviews, students' writterr worlL maps and photographs.Subsequentþ, the datawere coded to gentrate pattsns and meanings.Qualitative evidence shows that the CKCI\dand the teacher-researcher collaboration contribúed to the development of scientific literacy of grade five students in this particular conteú.Withrespect to the *what'' of science, studentsdeveloped an understanding of the concepts and principles of weather, as recommended by the Manitoba Education and Training.\¡/ith respect to the "how" of sciencæ, students comprehended that scientific knowledge is: a) partially based on human imaginæion and creativity, b) tentative, c) socially and culturally embedded, d) empirical, and e) developed througfu many methods.\ryiür respect to the'trvhy'' of science, students becarne aware of the intricate relationship ¿¡mong sciencq technology, society, and environment (STSE).The findings of this study have implications for teacher education and professional development.Based on theseresea¡ch findings, the following recommendations are made: .Science methods courses should offer instruction about the nature of science.o The frculy of scienc€ and education shorfd worktogetherto develop courses that aim at teaching science subject matter from multidisciptinary perspective.Most importantly, all pre-service teachers majoring in elementaqr and middle years education should be required to take such course(s). .Science educators and beginning teachers strould engage ín long-tenri, contemporary collaborative sfudies, which involve working with real students in authentic classroom situations-Aclmowledgments I world like to thank Dr. Arthur Stinner for his continuos support and consûuctive feedback and Dr. Thomas Booth for lending help whenever I needed it.
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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 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".