Implementing team-based learning in a large environmental chemistry course and its impact on student learning and perceptions
Bibliographic record
Abstract
Team-based learning (TBL) is an instructional strategy where students participate in a set of activities including, applying course concepts to real-life case studies in instructor-selected teams. Here, we describe how TBL has been incorporated into a 3rd year, large, environmental chemistry course and investigate the benefits of using this strategy. A combination of pre/post survey and coursework data were analyzed to understand: (1) What were student perceptions of TBL? (2) How did using TBL to deliver content influence student learning, measured by exam performance? (3) How did students’ team skills evolve? Post-survey results indicate that students perceived TBL as enhancing their interest in course content, creating real-world connections, and most helpful for achieving practical critical thinking skills. Student performance on TBL-related final exam items was significantly better (Mean = 73%, SD = 21%) than non TBL-related final exam items, (Mean = 65%, SD = 21%), despite the level of complexity being similar between the two categories. The pre/post survey results indicate that, as compared to the start of term, students reported being significantly more comfortable expressing opinions in group meetings ( t (78) = 4.25, p < 0.001, Cohen's d = 0.48), and leading group discussions ( t (78) = 3.11, p = 0.003, Cohen's d = 0.35), by the end of the term. The one-minute reflections (completed following the first and fifth TBL activities) indicated that there was a 14% increase (77% vs. 91%) in the number of students reporting on collective team decision making. This study demonstrates the wide-ranging positive impacts of TBL to student learning in a large Environmental Chemistry course all while enhancing active learning and applying chemistry concepts to relevant and real-life case studies.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".