Finding Evidence of Transfer with Invention Activities: Teaching the Concept of Weighted Average
Bibliographic record
Abstract
Coming to grips with the nature of measurement and uncertainty is often a common but implicit learning goal for many undergraduate physics labs.As educators, our intent is to have students be able to transfer their knowledge to novel situations: we aim to transform novices into experts.In the first-year physics laboratory at UBC, our approach to teaching weighted averagesamong other concepts-involves the use of invention activities.These invention activities actively engage the students, are intended to stimulate creative thinking, are particular in their brevity and high level of structure, and are designed to precede both explicit instruction and reinforcing practice.The merit of having students inspect the fundamental makeup of a problem before being taught to solve it has been shown as useful support for the formation of an initial orderly schema (i.e., preparation for future learning).The transfer of knowledge can be rather difficult to detect in a sequestered problem solving environment, but we claim to have found some evidence of its occurrence.In a situation for which a weighted average is required, we observe significantly more students paying attention to the uncertainty associated with the problem.Given the well-documented challenges associated with teaching the nature of measurement and uncertainty-and while many students still fall short of remembering or applying the correct formula of a weighted average-we interpret this transfer of a concept as a small victory.
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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 | 0.009 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 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".