The Unknown Exercise: Engaging First-Year University Students in Classroom Discovery and Active Learning on an Iconic Chemistry Question
Bibliographic record
Abstract
Scientific process thinking is usually lacking in first-year post-secondary general chemistry courses, as is a deep discussion of analytical techniques used to determine much of what we know about modern chemistry. A classroom activity is described here that brings the identification and characterization of a chemical unknown into the classroom, emphasizing self-directed, active, and teamwork learning within a social constructivist framework. In this way, the cognitive processes of identifying and characterizing an unknown can be emphasized separately from the psychomotor skills involved in the laboratory. Students work in pairs using self-directed learning to research the separation and characterization methods used by chemists. Each group advocates for a particular method, the class votes, and the instructor carries out the proposed method. The results are shown to the students (but not analyzed for them), and the cycle repeats until the unknown is identified. Students are assessed both individually and as a group. This activity was performed by 20–30 students in each of 3 years within two different first-year general chemistry contexts. Results show enhanced engagement in course and activity material and equivalent learning to lecture-delivered material based on assessment scores.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".