Problem-based learning: a student evaluation of an implementation in postgraduate engineering education
Bibliographic record
Abstract
This paper presents the student evaluation of a problem-based learning (PBL) implementation in the postgraduate engineering curriculum of a public university in Brazil. This investigation adopts a qualitative and collaborative design, as suggested when the research objective is to study phenomena in their natural settings in terms of the meanings people bring to them and when the data collected cannot be statistically handled easily. To this end, an instructional method based on PBL principles and activities was implemented in an administration theory course during one semester. The data utilized in this paper derive from participant observation and an end-of-term questionnaire in which the students were asked to evaluate the instructional method, its advantages and disadvantages, comment on some of its features, and give improvement suggestions. The student evaluations show that the approach used was very satisfactory and may have promoted the acquisition of knowledge as well as the development of some desirable skills and attitudes, such as teamwork and communication skills and respect for divergent ideas. Despite the favourable outcomes, the conclusion about the viability of using this instructional method in the context in question still depends on further consideration of some institutional and teacher-related issues.
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 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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".