Transforming Learning in Science Classrooms: A Blended Knowledge Community Approach
Bibliographic record
Abstract
In this study, I examined how science curricula designed based on the Knowledge Community and Inquiry (KCI) model (Slotta, 2007) would foster the development of knowledge communities in secondary school science classrooms. KCI situates scaffolded inquiry activities within a collective context. In two design iterations, I collaborated with high school teachers to design and implement grade-nine science curriculum units with the topic of Climate Change. Then, I probed the extent to which characteristics of classroom-based knowledge communities manifested in students’ collaborative inquiry activities and the product of their work.\nIn both iterations, students worked for approximately 8 weeks in a sequence of interconnected collaborative inquiry activities, creating digital inquiry artifacts. Two class sections engaged in iteration 1 and created wiki pages about the effect of climate change in Canada. They, then, used these wiki pages to examine the implications of climate change on certain industries. Five class sections engaged in iteration 2 where students identified important climate change-related issues and examined scientific aspects of those issues along with existing remediation plans. Knowledge co-constructed in this collaborative inquiry activity, contained in a Drupal platform, was used to propose improvement to existing remediation plans. Analyzing the process and the product of collaborative inquiry allowed me to examine the extent to which a knowledge community developed in each of the iterations.\nFindings from iteration 1 revealed that students needed tighter scaffolds during collaborative inquiry activities to stay focused on science connections. Additionally, epistemic scaffolds were added to the designed curriculum unit in iteration 2. Also, students were given regulative scaffolds to plan and monitor their collaborative inquiry. Findings from iteration 2 showed more science connections in co-constructed knowledge and higher amount of collaboration among students while constructing shared knowledge comparing to iteration 1.\nThis study provided further evidence of the effectiveness of KCI model to foster characteristics of knowledge communities in secondary school science classrooms. In addition to elaborating pedagogical and technological scaffolds that facilitated KCI curriculum units, recommendations were made for future research to improve existing scaffolds and, thus, progressing towards knowledge communities that are responsive to curricular expectations of science classrooms.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".