Active Learning Methods applied to an Environmental Awareness Course for CS majors
Bibliographic record
Abstract
The world has undergone major social changes in the last decades, leading us to a digital society. Although we have deeply changed the way we think, one subject has not changed in some countries such as Brazil: education. Brazilian students still sit in the classroom for hours while watching a professor speak. Even in undergraduate technology majors, such as Computer Science, the traditional learning methods remain and few innovations can be seen. This work shows a new curriculum for a discipline about environmental responsibility for undergraduate students in technology at a Brazilian university. The goal is to change the learning method using active learning, in which students are the protagonists of their own learning, while the professor acts only as a guide. Each class is 4 hours long and will be based on a different learning approach, therefore it must be self contained and well organized with a clear goal, so the professor can properly guide students to obtain the desired knowledge. This is a first step to change the way we see education to technological majors at our university, trying to bring innovation and new learning methods to a traditional environment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".