Using concept mapping for assessing and promoting relational conceptual change in science
Bibliographic record
Abstract
Abstract In this article, we adopted the relational conceptual change as our theoretical framework to accommodate current views of conceptual change such as ontological beliefs, epistemological commitment, and social/affective contexts commonly mentioned in the literature. We used a specific concept mapping format and process—digraphs and digraphing—as an operational framework for assessing and promoting relational conceptual change. We wanted to find out how concept mapping can be used to account for relational conceptual change. We collected data from a Grade 12 chemistry class using collaborative computerized concept mapping on an ongoing basis during a unit of instruction. Analysis of progressive concept maps and interview transcripts of representative students and the teacher showed that ongoing and collaborative computerized concept mapping is able to account for student conceptual change in ontological, epistemological, and social/affective domains. © 2004 Wiley Periodicals, Inc. Sci Ed 88:373–396, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/.sce10127
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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 | 0.028 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".