A Multilayered Approach to Understand and Imagine Doctoral Students’ Spaces of Learning
Notice bibliographique
Résumé
Aim/Purpose: The purpose of this qualitative study was to identify the main conceptualizations of learning space from doctoral students’ perspectives. The aim was to develop a participatory approach to make students’ multiple voices heard. Background: Doctoral experience is viewed as being influenced by social practices of the scholarly communities; learning space in this context is a collective resource that can be altered through imagination of its inhabitants. The intersection of Lefebvre’s Production of Space in architecture and situated learning theory in education enabled building an integrated conceptual framework to explore learning space of doctoral students in its complexity. Methodology: Three research questions reflected theoretical and practical aims. To answer them, drawing on Design Based Research, I developed multi-phased research through three sequential phases: questionnaire, Photovoice, and prototyping, which respectively addressed subjective, objective, and co-constructed aspects of learning spaces. Contribution: This study is one of the few studies that looks at doctoral students learning spaces within the literature of learning spaces. It supports the development of a participatory procedure to design learning spaces for doctoral students. Findings: Findings suggested that learning space is a layered multi-faceted phenomenon and a changing entity. Doctoral students believed that learning space is an indicator of support from doctoral programs and has a potential to improve and sustain their well-being. Recommendations for Practitioners: Inviting students to take charge of the configurations of their working environment is suggested for higher education institutions. Doctoral students imagined using movable, folding, and writable walls to create private spaces for individuals as well as collaborative workspaces. Recommendation for Researchers: Identifying the interactions between learning space and learning over a longer time frame both in undergraduate and graduate settings can help us view the campus through a spatial ecology model. Also, future research might examine a participatory approach to design and research on learning spaces around parallel partnerships with other research-intensive universities. Impact on Society: Findings from this study identified areas for future studies and actions suggesting implications for learning space studies for the U15 (Group of Canadian Research Universities) and U21 (the leading global network of research universities for the 21st century). Future Research: Considering the radical changes that COVID-19 has brought in how we work, collaborate, study, and engage in social events, it is vital for higher educational institutes to rethink their learning spaces for the post- COVID era to support students’ learning and their meaningful engagement in learning communities and learning spaces. Further exploration on learning spaces in post COVID era is needed to expand the empirical knowledge on learning spaces, and thus, to inform research scholars subsequent work in the educational field.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,024 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,008 | 0,030 |
| Communication savante | 0,012 | 0,018 |
| Science ouverte | 0,003 | 0,015 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».