Learning through service : community service learning and situated learning in high school
Notice bibliographique
Résumé
This dissertation explores the symbiotic relationship resulting from the merging of situated learning's socio-cultural conceptualization of the nature of learning with community service learning's ethos of service. As such, I enquired into the effects of the integration of situated learning as the conceptual framework, and community service learning as both an instructional methodology and educational philosophy. Specifically, through an ethnographic investigation I sought to discover the nature and outcomes of learning which result when high school students take their skills out of the classroom into the community to help solve authentic problems. The students with whom I worked were members of a high school computer technology class in which expectations were that they (the students) would combine learning with service by devoting ten to twenty hours to help a community agency solve technology-related problems. In this regard, eight different student groups applied their technology skills within a variety of school and community environments. Thereupon, I looked to ascertain not only if the students improved upon their already sufficient technical skills, but also what other abilities and knowledge of themselves and/or the world they appropriated. Thus, as per the defining features of situated learning and community service learning, I hoped to find evidence of learning in areas related to technological development, workplace knowledge and expertise, problem solving, group skills, personal and social maturity, and an ethos of service. Such learning occurred and, thus, I concluded that the integration of community service learning and situated learning in this technology classroom resulted in a symbiotic relationship in which the nature and specific outcomes of learning were 1) accounted for by situated learning and 2) enhanced beyond what would normally be expected in a non-service Information Technology Management classroom in the Province of British Columbia. Hence, the well documented and rigorously determined empirical findings: 1) argue that situated learning provides a viable theoretical framework for community service learning, 2) add empirical support to the learning claims of both situated learning and service learning, and 3) suggest a means of enabling education to become more responsive to the students and the community.
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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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,003 |
| 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 ».