Evaluating the effects of analogy enriched text on the learning of science: The importance of learning indexes
Bibliographic record
Abstract
Abstract Research has shown that differences in the prior knowledge of the participants and in the learning indexes adopted can explain why some studies show positive learning effects of analogy enriched text while others do not. In the present studies, these two factors were combined into one through the construction of a learning index that measured incremental positive changes in the participants' prior knowledge after reading an analogy enriched or no analogy text. A second learning index was also used to evaluate whether the participants created well‐formed conceptual models after reading the science text. These learning indexes were used in two studies in which the effects of analogy enriched versus no analogy text were compared on the learning of the scientific explanations of the day/night cycle and of the seasons. The participants were 3rd and 5th graders in the first study and 6th graders and college students in the other. Although only few of the participants learned the correct scientific explanation, those who read the analogy enriched text produced more incremental positive changes in their pretest explanations at posttest and delayed test and created more well‐formed conceptual models close to the scientific one than those who read the no analogy text. They also recalled more information and created fewer invalid inferences in their recalls. The results indicate that analogies can be used without reservation to facilitate the learning of science and have broader implications about how to evaluate the learning of science in general.
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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 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".