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"Lear.Ning Together". A case study examining the introduction of social collaborative learning supported by technology.

2010· article· en· W225439534 sur OpenAlexaboutno aff
Mark Curcher

Notice bibliographique

RevueGlobal Learn · 2010
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueOnline and Blended Learning
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésArgument (complex analysis)Social learningCollaborative learningLearning communityPedagogyLearning environmentPsychologyConstruct (python library)Cooperative learningMathematics educationComputer scienceTeaching method
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

There is strong evidence in the literature to support the argument that learning is a social and community activity and there are good arguments for the adoption of a social constructivist pedagogy to underpin learning activities. The author, teaching economics courses in an English as second language environment, was keen to move the delivery of courses away from a traditional knowledge transfer model to one in which students could socially construct their learning in a collaborative environment. To facilitate this a Ning community website was established, where students would maintain blogs and share and discuss resources and their learning. This case study looks at the results of this initiative over three cohorts of students, the lessons learned, the successes and failures and implications for further course design and development. In particular the case focuses on the student’s perception and attitudes to these changes and how they affect their learning. Introduction The Monetary Theory course at Dubai Men’s College (DMC) has been taught in a fairly traditional ‘knowledge transfer’ model since its inception. There is a strong argument in the literature that learning is a social activity and the author was keen to see if he could improve student engagement and thus encourage deeper levels of learning and enhance student success by using a blended learning model that included a much wider use of educational technology. The aim was to try and create a ‘community of learning’ among the students, with a particular focus on the sharing and discussion of resources using new social bookmarking technologies. The first cohort of students to use these new technologies studied the course in the second semester of academic year 2008/2009. The initial response to this, which focused on the use of the Diigo bookmarking tool, was reported in a paper presented under the ‘Best Practice” stream in October at E-Learn 2009 in Vancouver. Since then another cohort of students have taken the course and we have made some changes to the tools being used, thus this paper seeks to build on the earlier paper by including more details of student perceptions and reactions and include a larger sample of learners. The research is based on an action research model and results are gathered from an anonymous online survey completed by the students at the end of the course. Context The Monetary Theory course at DMC is taught in the final year of the Bachelors of Applied Science (BAS) in Business program. Students have normally taken four or five years to reach this stage of their studies and have previously taken a course in Micro and Macro Economics as well as a wide range of business courses. DMC is a part of the Higher Colleges of Technology (HCT), the federal vocational higher educational system for the United Arab Emirates (UAE). There are sixteen colleges spread across the cities of the UAE in the system, divided into male and female campuses. The students in this study are all male business students working towards a Bachelor of Applied Science in Business. Although chronologically mature, few DMC students display the characteristics of adult learners as described by Knowles’ Theory of Andragogy in (Moore and Kearley, 1996). Experience of working in this context for almost fifteen years suggests that in many ways they are still very much like child learners. Students expect teachers to make all the key decisions in relation to learning and tend to have a rather polarized view of the world where questions have answers that are either right or wrong.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,011
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,015
Score d'incertitude au seuil0,032

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0060,011
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0150,005
Communication savante0,0040,004
Science ouverte0,0030,006
Intégrité de la recherche0,0050,005
Charge utile insuffisante (le modèle a refusé de juger)0,0070,002

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.

Tête enseignante Opus0,011
Tête enseignante GPT0,323
Écart entre enseignants0,312 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2010
Routes d'admission1
Résumé présentoui

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