Learning with Multiple Representations: Infographics as Cognitive Tools for Authentic Learning in Science Literacy | Apprendre avec des représentations multiples: l'infographie de presse comme outil cognitif pour l'apprentissage authentique en science
Bibliographic record
Abstract
This paper presents a descriptive case study where infographics—visual representation of data and ideas—have been used as cognitive tools to facilitate learning with multiple representations in the context of secondary school students’ science news reporting. Despite the complementary nature of the two research foci, studies on cognitive tools and multiple representations have evolved independently. This is because research on cognitive tools has narrowly focused on technological artifacts and their impact on learning outcomes with less attention to learner agency and activity structures. This has created challenges of sustainably applying cognitive tools in classroom teaching and learning. Using data from a design-based research project where secondary school students created authentic infographic-based science news reports, this study demonstrates how infographics can serve as process-oriented cognitive tools for learning and instruction of science literacy in classroom contexts. Results have implications for the study and design of learning environments involving representations.Cet article présente une étude de cas où l'infographie de presse – offrant une représentation visuelle de données et d’idées – est utilisée comme outil cognitif pour faciliter l'apprentissage au moyen de représentations multiples dans le contexte de production de rapports scientifiques par des élèves du secondaire. Malgré la complémentarité des deux axes de recherche, les travaux sur les outils cognitifs et sur les représentations multiples ont évolué séparément. En effet, la recherche sur les outils cognitifs s'est strictement concentrée sur les artefacts technologiques et leur impact sur les résultats d'apprentissage mais a accordé moins d'attention à l’action des apprenants et aux structures des activités. Il en résulte des défis pour l’application durable d’outils cognitifs dans l'enseignement et l'apprentissage en classe. À partir de données issues d'un projet de recherche orientée par la conception (design-based research) dans lequel les élèves du secondaire ont produit des rapports scientifiques authentiques intégrant des infographies de presse, cette étude montre comment l’infographie de presse peut servir
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".