Notice bibliographique
Résumé
This paper explores the conception of learning as a complex phenomenon and the design of learning environments with insights from complexity research.We used design considerations from a complexity perspective to explore a structure that invited students to the collaborative design of games.We discuss our research on students' design of card/board games for their disciplinary and interdisciplinary learning in a western Canadian middle school.Our analysis highlights the variations in the students' designed games while they rely on their available resources to construct and communicate individual and collective meanings.We argue that engaging students in the design of games provide the opportunities for them to experience and understand the complex and interdisciplinary nature of any school topics.creativity and interest as well as new opportunities for collective learning (Davis & Sumara, 2006).Our use of complexity perspectives in this paper, is mainly focused on this pragmatic approach.We explore the affordances of learners' collaborative design of games based on our research in a western Canadian middle school, using the design conditions elaborated by complexity perspectives. Complexity perspectives of individual and collective learningComplexity research probes how diverse entities (e.g., living organisms) interact and shape systems with collective emergent behaviors (e.g., ecosystems) (Mitchell, 2009).These entities or agents adapt or learn, as they gain experience in interaction with one another and with new circumstances.Davis and Sumara (2010) discussed that complexity assesses the complementarities of the perspectives on individual and collectivie learning, elaborating on the interconnections of the systems of individual sense making and collective understanding.From this view, language, culture, social relations, and artifacts, which are of interest in sociocultural theories, act as the context of individual understanding, which emerges out of the interaction of sets of ideas.Complexity views indicate that such emergent phenomena cannot be pre-set, but more possibilities for learners' understanding and action might be stimulated through certain conditions.The following describes our design framework, informed by Davis and Sumara's ( 2006) conditions for emergent learning systems. Framework for Designing Learning Environments with Complexity PerspectivesA fundamental strategy for supporting learning communities has been suggested as setting enabling constraints.This notion is associated with complex systems' being "simultaneously rule-bound (constrained) and capable of flexible, unanticipated possibilities (enabled)" (Davis et al., 2015, p. 219).Designs for learning, then, entail a balance between sufficient structure to constraint the vast possibilities and sufficient openness to enable diverse responses.This approach underlies other interrelated conditions that include randomness, coherence, diversity, redundancy, and decentralized control (Davis & Sumara, 2006).We suggest that these conditions could move from a more conceptual level, at the top, towards more practical advice, at the bottom (Figure 1).Enabling constraints suggest a balance between coherence that supports the collective to keep its purpose and identity, and randomness that allows it to adapt and evolve (Figure 1, level 1).The elements in complex phenomena act within certain frames that both make possible and constrain their actions, similar to how individual players act within rules of games.Commonalities of agents, or the redundancy within the system, enable their interactivity and the system's sustainment.On the other hand, internal diversity is about agents' expressing their creativity, enabling the system to respond to new circumstances (levels
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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 | 0,001 | 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 | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».