Notice bibliographique
Résumé
This design-based study was the first empirical investigation of a new model of learning and instruction called Knowledge Community and Inquiry (KCI). In KCI, students are engaged as a learning community as they work on scaffolded inquiry activities that target specific science learning objectives. Although community-oriented approaches have been successful at the elementary level, there has been relatively little uptake of such methods at the secondary school level – particularly in science. The pedagogical framework of KCI addresses the challenges of community models by blending established inquiry based approaches with community-oriented pedagogy. This dissertation tested the validity of KCI by designing, implementing, and empirically evaluating a curriculum based on the KCI model. This was achieved through curriculum trials involving two separate cohorts of grade-ten biology students (n = 102; n = 112). The first implementation consisted of a two-week physiology lesson that engaged students in co-authoring wiki artifacts about human system diseases, which students then used as a resource for solving medical case studies. The second implementation, an eight-week lesson on Canada's biodiversity, was a deeper application of the model, and focused on students' collaborative processes during the construction of their wiki-based knowledge repository. In both cases, the curriculum was evaluated according to its design, enactment, and learning outputs, as evidenced by students' knowledge artifacts and performance on the final exam. Technology scaffolds ensured that students focused on the physiology and biodiversity science curriculum expectations. Analyses of the data revealed that KCI engaged students in collaborative learning processes that were characteristic of a knowledge community. Additionally, final exam scores demonstrated increased learning performance when compared to those from previous years where students did not participate in KCI. The findings from this research provide the first empirical support for KCI, and demonstrate its potential for engaging secondary science students in the kinds of collaborative inquiry processes of authentic knowledge communities. This dissertation provides insight into the conditions necessary for such engagement, and contributes design recommendations for blending knowledge community and inquiry in secondary school science curriculum.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 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 tête enseignante, 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 ».