The Joint Work of Connecting Multiple (Re)presentations in Science Classrooms
Bibliographic record
Abstract
ABSTRACT The aim of this study is to advance current understanding of the transactional processes that characterize students’ sense‐making practices when they are confronted with multiple representations of scientific phenomena. Data for the study are derived from a design experiment that involves a technology‐rich, inquiry‐based sequence of activities. We draw oninteraction analysisto examine the work by means of which a group of upper secondary school students make sense of a number of different ways in which a physical phenomenon—a phase transition—is presented to them. Our analytical perspective, grounded in a cultural‐historical framework, involves scrutinizing how the different materials emerge and evolve as signifiers for something other than themselves during teacher–student and student–student transactions. This approach allows us to trace the emergence of students’ interpretations of the relations between phenomena and their diverse presentations without committing to any preconceived notion of what these presentations stand for. We describe how students’ bodily and pragmatic actions become reified in conceptual terms and how these relate to lived‐in experiences rather than to formal underlying concepts. Findings are discussed with regard to the central role of body and praxis in research on learning science with multiple representations.
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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 | 0.013 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".