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Record W4406061281 · doi:10.1504/jdr.2024.143687

Assessing the impact of experiential teaching methods on learning outcomes in design studies for barrier-free built environments

2024· article· en· W4406061281 on OpenAlexaff
Deepti Reddy, Kanna Siripurapu

Bibliographic record

VenueJ of Design Research · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Environments and Student Outcomes
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsExperiential learningComputer scienceHuman–computer interactionPsychologyEngineeringMathematics education

Abstract

fetched live from OpenAlex

Incorporating inclusive design principles into built environments is important to promote universal accessibility. Designing inclusive spaces requires a good understanding of users and their needs. In this context, the present study aims to design a framework for teaching barrier-free built environments to undergraduate design students through experiential learning. Role play and virtual reality exercises were integrated into the curriculum to encounter and examine the complexities of barriers in the built environments. Role-play exercises allow students to personally experience the challenges encountered by physically challenged people when accessing barriers in built environments. Virtual reality technology provides the experience of visually challenged users. The outcome of the experiential teaching method suggests a change in the students' attitude, knowledge, behaviour, and sensitivity toward barriers in built environments. Our observations suggest that integrating experiential and cognitive learning frameworks encourages more compassionate and inclusive behaviour among the pupils.

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)0.016
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.173
Threshold uncertainty score0.849

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.447
GPT teacher head0.653
Teacher spread0.206 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2024
Admission routes1
Has abstractyes

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