Student Perceptions of Two-Stage Testing in an Undergraduate General Chemistry Course: For Whom is the Experience Good and Bad?
Bibliographic record
Abstract
Two-stage exams are a collaborative learning practice in which students complete part of a summative assessment in a learning team. We investigated what students like and dislike about two-stage quizzes and midterm exams using an open-ended survey to identify key themes and analyzed the relative frequencies of these themes and compared the frequency of responses based on participants’ gender, race/ethnicity, or access to learning accommodations. Overall, we found students overwhelmingly prefer the two-stage assessment, a feeling driven by positive feelings toward the team portion of the exam. Given that historically underrepresented student groups (based on race, gender, sexual orientation, physical ability, socioeconomic status, etc.) traditionally face achievement gaps in chemistry education, we were also motivated to learn if two-stage exams were viewed as an equitable teaching practice by students. Comparisons of the proportions of participants giving thematic responses according to Gender, Race/Ethnicity, or Learning Accommodations status revealed no significant ( p < 0.05) differences between populations, except for strong evidence that students who accessed accommodations were more likely to report the two-stage exam helped their grades ( p = 0.018). Additional results suggest there may be some minor differences in how students experience two-stage exams based on their demographic profiles, but further research is needed.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".