A Tiny Adventure: the introduction of problem based learning in an undergraduate chemistry course
Bibliographic record
Abstract
Year 1 of the chemistry degree at the University of Leicester has been significantly changed by the integration of a problem based learning (PBL) component into the introductory inorganic/physical chemistry module, 'Chemical Principles'. Small groups of 5-6 students were given a series of problems with real world scenarios and were then given the responsibility of planning, researching and constructing solutions to the problem on a group wiki hosted on the Universty’s Virtual Learning Environment (VLE). The introduction of PBL to the course was evaluated both quantitatively and qualitatively. Class test and exam results were analysed and compared with those achieved in previous years (i.e. before the introduction of PBL). It was found that student performance was at least as good as it had been before the introduction of PBL. Retention figures after PBL had risen sharply (not one PBL student dropped out of the course during the first term). Student and staff feedback was also collected for qualitative analysis of the impact of the change. Combining these findings showed that students appeared to show an improvement in, and recognition of the acquisition of, transferable skills and that group work on immediate arrival at university (representing an opportunity to use social skills within an academic exercise) led to high student retention within the PBL cohort.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".