Promoting Student Metacognition Through the Use of Exam Wrappers in a Second-year Analytical Chemistry Course
Bibliographic record
Abstract
Building metacognitive strategies is crucial for undergraduate students to understand and retain topics more readily, be more motivated to learn, and get prepared for postgraduation careers. Building metacognitive skills can often come from trial and error as students progress through their undergraduate degrees. Formal support in developing these skills can come in the form of postexamination reflection surveys, commonly called exam wrappers . Herein we report the development and deployment of exam wrappers in a large, second-year analytical chemistry course serving 344 students annually in two sections of approximately with 178 and 166 students in the first and second semester, respectively. We felt this environment was an excellent match for this metacognitive activity as second-year analytical chemistry can be a challenging learning environment for students making the transition from novice-level general chemistry at the first-year level to a more specialized environment at the second-year level. Our exam wrapper had outstanding uptake by the students, with over 90% of students completing both exam wrappers for the first and second examinations and 93.5% of students offering positive or neutral feelings about the exam wrappers helping them prepare for future examinations through self-examination of their studying practices. Between the first and second examinations, approximately one-third of students reported a higher grade, with 38.5% of students reporting doing more practice problems, 19.2% of students starting studying earlier, and 21.2% of students completing the practice examination when they had not before. This low instructor workload, high-impact tool can be incorporated into any course in chemistry where students are assessed using assignments or examinations.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".