A Multilayered Approach to Understand and Imagine Doctoral Students’ Spaces of Learning
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.030 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".