A PRAXEOLOGICAL ANALYSIS OF PRE-SERVICE ELEMENTARY TEACHER-DESIGNED MATHEMATICS COMICS
Bibliographic record
Abstract
Mathematical and didactic knowledge presented in mathematics textbooks and other resources, like mathematics comics (MCs), needs to be evaluated from a lens of appropriate theoretical framework in mathematics education before it can be used as a medium for teaching and learning mathematics.Therefore, this study investigates mathematical and didactic competencies that were reflected in MCs designed by pre-service elementary teachers. The framework for analysing mathematical knowledge embedded in these MCs is based on the Anthropological Theory of the Didactic, specifically a praxeology. This study utilized a content analysis technique within a qualitative approach. Thirteen MCs were analysed using a praxeological analysis; the type of task and techniques (praxis block) as well as the possible technology and theory (logos block). The findings demonstrate that the mathematical praxeologies embedded in MCs belong to five mathematical domains, namely numbers and operations; number theory; fractions, decimals, and percentages; ratio and proportion; as well as measurement. Additionally, the analysis revealed that seven of these MCs were related to a single domain, while the others belong to two or three mathematical domains. Concerning the mathematical praxeologies, most of MCs focus on presenting the practical blocks, the type of task and the techniques, while only a few could provide the theoretical lens to justify the practical blocks.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".