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Record W3120525702 · doi:10.1108/arch-10-2020-0245

Speculations on the post-pandemic university campus – a global inquiry

2021· article· en· W3120525702 on OpenAlexaff
Jay Deshmukh

Bibliographic record

VenueInternational Journal of Architectural Research Archnet-IJAR · 2021
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsIBI Group (Canada)
Fundersnot available
KeywordsDistancingSociologyQualitative researchHigher educationPedagogyPandemicSpace (punctuation)PsychologyMathematics educationCoronavirus disease 2019 (COVID-19)Public relationsPolitical scienceComputer scienceSocial scienceMedicine

Abstract

fetched live from OpenAlex

Purpose The pandemic-induced global shift to remote learning calls for rethinking the foundations of design for higher education. This watershed moment in global health and human interaction has accelerated changes in higher education that were long emergent and amplified specific deficiencies and strengths in pedagogical models, causing institutions to reevaluate current structures and operations of learning and campus life as they question their vision and purpose. Since physical space has largely been taken out of the equation of university life, it is evident that fresh design research related to this new normal is required. Design/methodology/approach This qualitative research study speculates on new possibilities for the future of campus, based upon insights and inferences gained from one-on-one interviews with faculty and students in multiple countries about their personal experiences with the sudden shift to the virtual classroom. The longer the mode of physical distancing stretched through Spring 2020, these phone and web-enabled dialogues – first with faculty (teachers) and then with students (learners) – lead to a deeper, more nuanced understanding of how the notion of the campus for higher education was itself morphing in ways expected and unexpected. Findings At the heart of this study lies the question – Has COVID-19 killed the campus? This study suggests that it has not. However, campuses are now on a path of uneven evolution, and risk shedding the good with the extraneous without eyes-wide-open rethinking and responsive planning. This two-part qualitative analysis details the experiments and strategies followed by educators and students as the pandemic changed their ways of teaching and learning. It then speculates out-of-the-norm possibilities which campuses could explore as they navigate the uncertainty of future terms and address paradigm shifts questioning what defines a post-secondary education. Research limitations/implications This paper draws inferences from discussions limited to the first 100 days of the pandemic. This on-the-ground aspect as the pandemic continues is its strength and its limitation. As Fall 2020 progresses across global campuses, new ideas and perspectives are already reinforcing or upending some of this paper's speculations. This researcher is already engaged in new, currently-ongoing research, following up with interviewees from Spring 2020, as well as bringing in new voices to delve deeper into the possibilities discussed in this paper. This follow-up research is shaping new thinking which is not reflected in this paper. Originality/value Design practitioners have long-shaped campuses on the belief that the built “environment is the third teacher” and that architecture fosters learning and shapes collective experience. Educators recognize that a multiplicity of formal and informal interactions occur frequently and naturally across campus, supporting cognitive and social development, collegiality and well-being. Even today's digital-native-students perceive the inherent value of real interpersonal engagement for meaningful experiences. This research study offers new planning and design perspectives as institutional responses to the pandemic continue to evolve, to discover how design can support what lies at the core of the campus experience.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.666
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.168
GPT teacher head0.498
Teacher spread0.330 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations50
Published2021
Admission routes1
Has abstractyes

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