The resurgence of everyday experiences in school science learning activities
Bibliographic record
Abstract
Abstract Science education can be alienating for students, as it is apart from the mundane world with which they are familiar. Science education research has approached the gap between everyday understandings and science learning largely as a challenge arising while learning about science concepts and the kinds of instructional approaches that may support this. However, the forms of everyday ways of relating to the world fundamentally expand beyond conceptual understandings. In this study, we use data from an outdoor science learning setting to examine a range of non-conceptual but culturally possible and intelligible ways in which students actually connect science learning processes to their everyday world and its characteristic commonsense understandings. Our study shows how students’ (a) spontaneous embodied explorations, (b) humor in all of its bodily and grotesque forms, and (c) narrative representation and interpretation of the world are used to contextualize science learning, namely its environment and content, within their familiar world. We show how students draw on these fundamental cultural forms of understanding the world even without particular instructional support while, at the same time, completing their science tasks according to the goals set by their teachers. Our findings suggest that the ways in which students connect their everyday world with science learning do not have to be explicitly related to the particular conceptual learning goals but can parallel conceptual learning while contextualizing it in affectively meaningful ways.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| 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".