Participation in the Classroom: Classification and Assessment Techniques
Bibliographic record
Abstract
Class participation and active engagement are both critical components for student success in a variety of classroom settings. This is especially true in philosophy classrooms where students are expected to develop and refine their ability to critically and productively engage with the literature being studied while also participating in conversations with their peers. Including participation expectations in classes is a common strategy, used by teachers, for developing and honing important skills in students. In requiring participation in their respective courses, teachers seek to refine and cultivate critical thinking and communication skills in their students. This workshop begins with an overview of research that, on the one hand, examines different strategies for effectively encouraging student participation and, on the other, also provides recommendations for broadening our definition of student participation in the classroom. Following the literature review, I will provide an active learning, self-assessment tool for evaluating course participation (this tool can be adapted to larger classroom settings and contexts, as well) that, I suggest, will be of benefit to teachers and students alike.
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 | 0.037 | 0.115 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".