The Impact of Two-Stage Testing and Other Tutorial Co-Learning Activities on Cohort Cohesion and Learning Outcomes in a Large-Enrollment Undergraduate Course
Bibliographic record
Abstract
Large enrollments in undergraduate courses pose several teaching and learning challenges that impact students’ learning experience and performance. Implementing co-learning activities in tutorials of large courses can help mitigate these challenges and improve the learning environment. One type of collaborative learning activity that has become increasingly popular is two-stage testing but there are limitations to how two-stage testing has been conducted. We undertook a study to elucidate whether our modified two-stage testing protocol and other co-learning activities performed in tutorials can enhance the learning experiences of undergraduate students and foster a sense of community in a large-enrollment research methods course. The specific aims of our study were to: 1) Assess whether co-learning activities including two stage testing in tutorials improves learning outcomes and fosters cohort cohesion in a large-enrollment junior undergraduate science course. 2) Evaluate the impact of our modified two-stage testing approach on student learning and long-term retention. To assess cohort cohesion students were asked to complete a survey and were invited to participate in focus groups. Results indicated that tutorials did foster cohort cohesion among students in the tutorial. The tutorial activities helped scale down the course size and connect with their peers. We tested our modified two-stage testing protocol by administering a two-stage test (an individual test consisting of short-answer questions followed by a group test that was comprised of a subset of the individual test questions that was completed during tutorials). Approximately three months after the individual test, a retention test was administered. Student grades were significantly higher in group tests compared to the individual tests. Interestingly, students on average scored 6.2% higher on the retention test questions that were from the group test, compared to questions that were only on the individual test. These results support the idea that group tests help improve student retention. Students reported tutorials and two-stage testing to be a positive learning experience.
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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 | 0.018 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".