The Joyful Noise of Learning: Active Learning Strategies for Large Classes
Bibliographic record
Abstract
We couldn’t stand the crossed arms and blank faces of the students in our large first year course any longer so we completely revised the course to highlight experiential learning opportunities and boy are we glad because it turned out really well. We designed a new course to centre on a Sociology Workbook that is similar in style to a hands-on science lab manual. Students buy the workbook with their texts, engage in active in-class learning projects outlined in the workbook, and then record their results to be handed in at the end of each class. In addition, the course now includes real-world case studies and activities that engage students in practical and applied examples of the theoretical issues addressed in the course material. We also included analyses of contemporary best-selling books that address relevant social issues so that students have the opportunity to participate in current debates about issues of social importance. A research assignment was developed reflecting the traditional methodologies of our discipline, which provides the students with the opportunity to conduct primary research and develop valuable research and analysis skills.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".