Innovative Methodologies for 21st Century Learning, Teaching and Assessment: A Convenience Sampling Investigation into the Use of Social Media Technologies in Higher Education
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Notice bibliographique
Résumé
The advent of the Web as a social technology has created opportunities for the creation of informal learning environments, which have potential for innovative methodologies in learning, teaching and assessment. However, as Wolfe (2001) admonishes, “contrary to the rhetoric of cheerleaders, the Web places greater demands on students than traditional modes of instruction” (pp. 2 – 3). The pedagogical potential of these high tech, e-skilling, multimedia digital technologies to revolutionize teaching, learning and assessment will only be realized if the underlying theoretical foundations are well articulated and supporting evidence is provided through well-designed empirical research studies. This paper contributes to these two prospects in two ways. First, it articulates the theoretical framework drawn from the work of luminaries in pedagogy that posits cooperative, social learning strategies, as potential methodologies for effective pedagogy. Second, it describes the results of a convenience sampling case study, which investigated the use of cutting-edge social media technologies, namely Google + Discussion Circles, (GDCs), to shed some light on how the use of these social media technologies supported teaching, learning and assessment activities for 2 nd year Bachelor of Education students at a university in Australia. The research found, inter alia, that when students were given the opportunity to learn using GDCs, the majority took advantage of the academic, social and structural dynamics created by these technologies in many ways that supported their learning, assessment activities and overall academic outcomes. The research-based evidence shows that the benefits included high participation rates, great levels of interpersonal interactions among participants, pedagogically rich posts in the GDC streams, metacognitive processing, peer mentoring, ambiguity tolerance, anxiety and motivation. There was also considerable student engagement, exploration of issues, elaboration of what was being discussed in the GDCs, evaluation and explanation, consistent with Bybee et al. (2006) 5E Instructional model for supporting and maximizing students’ learning. The evidence leads to the recommendation that pedagogues at universities and other institutions of higher education should explore opportunities for utilizing selected social media technologies in their pedagogical practices, because, if properly planned and implemented, these technologies appear to have potential so support effective learning, teaching and assessment in the 21 st century. Further research on this topic could also be very beneficial.
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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 | 0,002 | 0,004 |
| 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 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 découle