19. Encouraging Undergraduate Class Participation: A Student Perspective
Bibliographic record
Abstract
Undergraduate classes typically involve a professor lecturing to 100 or more students. Too often, this results in minimal opportunities for student participation. Positive reinforcement was used to promote student participation (i.e., defined as relevant comments or questions) in a second-year psychology class (N = 97). Class participation was measured for five weeks in two 80-minute lectures per week. Baseline was collected in two lectures. In the unaware phase for two lectures, paper tickets were given without explanation to students who participated. Students were then informed that tickets were given for class participation, and would be entered into a draw for gift certificates. Data were collected for four lectures in this informed condition. Final baseline consisted of two lectures with no tickets distributed. Student attendance was recorded. Frequency of instructor questions remained relatively consistent. Class participation rose from 38 relevant comments and questions per week during initial baseline, to 47 during the unaware phase, 52.5 during the informed phase, and 60 for final baseline. Positive reinforcement was associated with increased class participation overall, but with little change for students with high initial participation. Students said they enjoyed and benefited from the class participation activity.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".