The financial numeracy afforded in secondary mathematics: A study on the textbooks, perceptions and practices of teachers in Quebec, Canada
Notice bibliographique
Résumé
Financial education has become increasingly more important worldwide However, this matter has yet to find mainstream attention in mathematics education. A mathematics education perspective is necessary because, despite the role of mathematics in financial education being recognized by researchers and institutions, we still lack a conceptual framework to understand how mathematics and financial education interact and contribute to each other. In this research, I used the concept of financial numeracy to refer to the knowledge, confidence and ability to use numerical information in financial situations. I mobilized this concept through three dimensions of financial numeracy: contextual (financial situations as a context to teach mathematics), conceptual (mathematics as a way to make sense of financial concepts) and systemic (mathematics and financial situations connected to other epistemological systems: ethics, values, beliefs, politics, etc.).With increasing efforts to incorporate financial numeracy in curricula around the world, the question of how to teach it still lacks an appropriate answer. I addressed this issue by focusing on financial numeracy from the perspective of one key stakeholder: mathematics teachers from Quebec, Canada. To understand their context and inform future professional development to teachers, I asked three research questions: 1. What are the representations of financial numeracy in the didactical materials available to secondary mathematics teachers in Quebec? 2. What are their perceptions of financial numeracy in the context of a professional development session? 3. What aspects of financial numeracy do they emphasize in their teaching? I used quantitative and qualitative methods to analyze financial numeracy tasks from 40 textbooks, the perceptions shared by 35 teachers in six professional development focus groups, and the teaching practices enacted by six teachers when they implemented financial numeracy lessons.The results showed a diverse range of financial numeracy approaches. In the textbook collections, financial numeracy tasks comprised of short word problems that emphasized the explicit mathematical content, multi-step problems that connected mathematical processed to their real-life meaning, and open-ended tasks that incorporated students’ personal experiences and perspectives into the justification of the problems. In the professional development sessions, the perceptions of teachers were informed by their professional and personal stances toward financial numeracy, which in turn revealed two orientations toward this concept: those who were concerned about the connections between financial numeracy and mathematical concepts from the curriculum, and those who expressed a tension between their desire for students to be financially numerate and their lack of knowledge of financial concepts. Finally, the classroom data revealed four main teaching practices enacted by teachers in financial numeracy lessons: emphasizing procedural fluency in financial situations, using technology to focus on interpreting results of financial problems, sharing personal experiences regarding financial situations, and providing practical advice on financial matters to students.The results of this research contribute to advancing our understanding of this emerging concept by unifying the different approaches used in research into one coherent framework of financial numeracy. Overall, this concept is a powerful conceptual tool to think about the ways of introducing and integrating financial situations in mathematics classrooms. It provides insights to what teachers can afford to do and justify their choices based on their own perspectives and the institutional affordances of the school system. Financial numeracy fundamentally concerns mathematics educators and this research has shown possible paths they can build to have their voices heard in this matter
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,012 | 0,005 |
| Communication savante | 0,004 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».