Factors Influencing the Adoption of Problem-Based Learning for Building Technology Education in Developing Countries
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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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".