Socialization and Cognitive Apprenticeship in Online Doctoral Programs
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Notice bibliographique
Résumé
Online doctoral programs are gaining in popularity, both among students and institutions. However, research to date on the effectiveness and popularity of such programs has looked largely at either quantitative measures of student satisfaction or of administrative effectiveness and design. Further, previous research has also tended to focus on the early part of doctoral study; in specific, the coursework. This qualitative study reports findings from four online doctoral programs in one UK university, contributing to the literature in two important ways. First, we aim to look specifically at current and recently graduated students’ experiences of doing their thesis using a demographic and experiential survey. This will be followed up by in-depth interviews to better understand the kinds of academic experiences and knowledge they both bring to, and receive from their program. Second, we aim to analyse the data through the lens of cognitive apprenticeship to help us better understand the individual trajectories of students in the thesis portion of their programs. By so doing, this research will contribute both theoretically and practically to our understanding of student experience of the thesis process in online doctoral programs. In particular, we conclude that there is a lack of knowledge and frameworks for how to design online/distance post-graduate programmes that best support the cognitive apprenticeship model. We suggest a shift in the research agenda on this issue: Perhaps, the first step towards a more effective direction is to focus less on quantitative measures for success, like enrolment statistics or graduation rate but rather to employ qualitative judgements for the evolution of the post-graduate experience. What might be the guidelines for such qualitative judgments? The answer may lie within the principles of Networked Learning: knowledge is not confined to an individual; rather, it is distributed across individuals within the environment. That is, learning is not an in-the-head phenomenon but a matter of engagement with, participation in, and membership to a community. We argue that it is through this notion of learning that we may develop a more effective framework to reconceptualise the theory and practice of online/distance post-graduate education within the cognitive apprenticeship model of learning.
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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 | 0,000 | 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)
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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