Concept mapping as a tool for monitoring student learning in science
Bibliographic record
Abstract
Concept maps consist of the production of pictorial representations that depict learners' knowledge about concepts (Novak 1990). Concept maps are usually developed during instruction, and typically comprise students' prior knowledge, real-world objects, and related scientific concepts. Their use shows how an individual sees the relation between things, ideas, or people. Concept maps can be used both as evaluation instruments and teaching aids. According to White & Gunstone (1992), the construction of concept maps is particularly useful for group activity, since they aid conceptual development by encouraging meaningful discussion and reflection. There have been a number of reports in the science education literature demonstrating the positive effects of the use of concept mapping for science instruction. Roth & Roychoudury (1993) found that the use of concept maps helped Canadian pre-service and in-service primary teachers to construct scientific knowledge and develop favourable attitudes to meaningful learning in science. In the UK, Pendlington, Palacio & Summers (1993) have also recommended the use of concept maps in the in-service training of primary level teachers. Adamczyk & Wilson (1996) report that concept mapping was valuable in diagnosing of practicing science teachers' alternative conceptions in physics, and they subsequently evaluated the effects of in-service training activities knowledge and understanding of certain aspects of physics.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".