Concepts first, jargon second improves student articulation of understanding
Bibliographic record
Abstract
In this experiment, students in a large undergraduate biology course were first exposed to the concepts without new technical vocabulary ("jargon") in a pre-class reading assignment. Their learning of the concepts and jargon was compared with that of an equivalent group of students in another section of the same course, whose pre-class reading presented both the jargon and concepts together in the traditional manner. Both groups had the same active-learning classes with the same instructor, and then completed the same post-test. Although the two groups performed the same on the multiple choice questions of the post-test, the group exposed to concepts first and jargon second included 1.5 times and 2.5 times more correct arguments on two free-response questions about the concepts. The correct use of jargon between the two groups was similar, with the exception of one jargon term that the control group used more often. These results suggest that modest instructional changes whereby new concepts are introduced in a concepts-first, jargon-second manner can increase student learning, as demonstrated by their ability to articulate their understanding of new concepts.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".