Electric Kasetsu! addressing misconceptions about basic electricity through the development of an engaging “predict-observe-explain” sequence
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
In the field of conceptual change research and activity, the need to generate cognitive conflicts has often been seen as central to the initiation of any change process. One strategy that science teachers often use to generate such conflicts is to ask their students to make predictions about the outcome of demonstrations or experiments whose results may surprise them. Predict-Observe-Explain (POE) strategies have thus been proposed and remained popular, and although they can be carried out productively, they are often limited to demonstrations which, although very informative, they are sometimes presented as one-off events, short sequences or in isolation from other relevant content. This article presents the results of the development of a Kasetsu-inspired complete series of 17 POE-type problems, sequenced in such a way as to bring into conflict 13 possible misconceptions in the field of basic electricity that young students may have.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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