Visual Representation Construction for Collective Reasoning in Elementary Science Classrooms
Bibliographic record
Abstract
There has been a recent increase in research interest in the ways that visual representation is used to facilitate students’ understanding in science classrooms. Yet while many studies have explored individual students’ drawing, few studies have looked into drawing as a collective tool to engage students in thinking and talking together in science classrooms. This study employed a case study approach to understand some of the possibilities for visual representation construction as a collective reasoning tool. By examining two cases of visual representation construction during classroom talk in two elementary science classrooms, this study demonstrates how teachers use visual representation as an instructional strategy, and how visualization engages students’ reasoning, meaning making, and social interactions. We selected two cases that demonstrate the emergence of the teacher’s and students’ drawing activities and analyzed each with a focus on the interactions that occur during the construction of visual representation and how this interaction promotes scientific reasoning and meaning making. For the case analysis, three researchers reviewed the video cases separately several times, and then collectively developed in-depth discussion to bring forth possible themes. The findings include (a) that there were common grounds of visual representation established for collective reasoning, and (b) that visual representations expanded knowledge and reasoning from the individual to social level, thus playing a critical role in students’ reasoning and knowledge building during classroom talk. Pedagogical questions and reflection are discussed for further research on visualization as a cognitive and social tool in classrooms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".