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A Preliminary Study on Problem Based Learning and its Implementation in Architectural Education

2008· article· en· W565280520 on OpenAlexaboutno aff
Fadzidah Abdullah, Maheran Yaman

Bibliographic record

VenueThe Journal of the World Universities Forum · 2008
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceMathematics educationPsychology

Abstract

fetched live from OpenAlex

This paper explores Problem Based Learning (PBL) educational approach and investigates a case study where PBL was implemented in architectural education. It aims to evaluate the appropriateness of Problem Based Learning for the pedagogical improvement and development of architectural education. A preliminary exploration of PBL is essential to understand what the educational approach could offer in improving architectural education. It is found that PBL has been recognised as an innovative educational approach and shown to have the potential to enhance the education process and its outcomes. Many discipline of tertiary education such as engineering, medicine, management, and law, have adopted the educational approach since it was introduced in Medical Faculty of McMaster University, Canada, in the late 1960�s. However, implementation of PBL in architectural education is limited within two (2) schools of architecture only: in Technical University of Delft (TUDelft), the Netherlands, and in University of New Castle, Australia. Further investigation on methods of PBL implementation in TUDelft is carried out to evaluate PBL implementation in architectural education. It is hope that this investigation provides basis to other schools of architecture on what direction architectural education should go in improving the pedagogy of architectural education as a whole.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.796
Threshold uncertainty score0.159

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.006
GPT teacher head0.232
Teacher spread0.226 · 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 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

Citations1
Published2008
Admission routes1
Has abstractyes

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