Exemplification in science instruction: Teaching and learning through examples
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.045 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it