Engaging Students in Science: The Potential Role of “Narrative Thinking” and “Romantic Understanding”
Bibliographic record
Abstract
Engaging students in science and helping them develop an understanding of its ideas has been a consistent challenge for both science teachers and science researchers alike. Such a challenge is even greater in the context of the “Science for All” curriculum initiative. However, Bruner’s notion of “narrative thinking” and Egan’s “romantic understanding” offer an alternative approach to teaching and learning science, in a way that engagement and understanding become a possibility. This chapter focuses on students’ “narrative mode of thought”, as a bridge to understanding science—which has traditionally been based more upon the use of logico-mathematical thinking in the upper grades—and on a distinctive form of understanding the world, characteristic of students of the age range from eight to fifteen years. This latter form of understanding, that the educational theorist Kieran Egan calls “romantic understanding”, has features that can be readily associated with the natural world and its phenomena. Therefore its development could be fostered in the context of school science learning, and in a way that the narrative mode would also be taken into consideration.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.038 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".