Exemplification in science instruction: Teaching and learning through examples
Bibliographic record
Abstract
Although the practice of giving examples is central to the effective teaching and learning of science, it has been the object of little educational research. The present study attends to this issue by systematically examining the exemplification practices of a university professor and his students' learning experiences during a biology lecture on animal behavior. It is reported that the science instructor provided students with a series of procedural, conceptual, and analytical examples. Each type of exemplification was characterized by a unique focus, form and degree of dialogism. These examples promoted student acquisition of specialized scientific language and engagement in varied types of argumentation: inductive reasoning by parallel cases, inductive reasoning by causation, inductive generalization, and deductive reasoning. Furthermore, students' experiences learning from examples were contingent upon their performance of parallel instructional activities such as text reading and note-taking. Based on these findings, we argue for the importance of promoting student development of exemplification literacy (the ability to critically assess the use of examples in scientific communication) and the need for science instructors to provide students with opportunities not only to learn science concepts through examples but also to learn about the nature of scientific exemplification itself. © 2016 Wiley Periodicals, Inc. J Res Sci Teach 53:737–767, 2016
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".