Factors Influencing the Adoption of Problem-Based Learning for Building Technology Education in Developing Countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Problem-based learning (PBL) is still an emerging paradigm of educational instruction in the current era. Nevertheless, PBL has been successfully adopted in many developed countries like Japan, Canada, and China. PBL has been claimed to have numerous benefits when adopted, ranging from a more motivated autonomous learner to acquiring lifelong learning skills. However, there are influencing factors that may hinder the adoption. Hence, this study explores the factors influencing the adoption of PBL in Building Technology Education (BTE) in Nigeria's Higher Educational Institutions (HEI). The study adopted a quantitative method, and the instrument used in collecting data was a questionnaire administered to 117 respondents from the Federal College of Education Gusau. Quantitative data were analyzed using descriptive statistics. All respondents agreed that all the items in the questionnaire influence the adoption of PBL in BTE. Notably, course design, and infrastructure readiness are major factors that influence the adoption of PBL
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it