A course design workshop as a possible path from a content-centered to a learning-centered teaching
Bibliographic record
Abstract
In order to meet the needs of a constantly changing Society, the Universities need to constantly improve their processes of teaching and learning. To do so, it is essential that professors are fully committed and well prepared to teach aiming at students learning, instead of content delivery. Faculty development programs might be helpful to support the institution and the professors in this way. Since designing these programs is a challenging task, we intend to contribute with faculty developers by reporting our experience here. We have adapted a course design workshop developed at McGill University to our context at PUCPR, in Curitiba, South of Brazil. During the workshop, the participants had to write a new syllabus of their course, elaborate a concept map, both of them with only the essential aspects for learning. They had to define the learning outcomes and only afterwards to choose active methods to help students achieve them. Throughout the whole process, participants gave feedback to each other. The activities of the workshop, along with the fruitful discussions among professors of different backgrounds helped professors to view the content as something that supports the development of learning outcomes. Therefore, we conclude that this workshop has opened the way to methodological innovations that develop learning of higher cognitive dimensions, since the professor has established more challenging expectations for the students when writing the new teaching plan.
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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 | 0.028 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".