‘Triangulation:’ an expression for stimulating metacognitive reflection regarding the use of ‘triplet’ representations for chemistry learning
Bibliographic record
Abstract
Concerns persist regarding high school students' chemistry learning. Learning chemistry is challenging because of chemistry's innate complexity and the need for students to construct associations between different, yet related representations of matter and its changes. Students should be taught to reason about and consider chemical phenomena using ‘triplet’ representations. A meta-language to discuss chemistry learning with students regarding these representations and their use is therefore necessary. This paper reports on a classroom intervention in which the teacher used the term ‘triangulation’ as an expression to stimulate metacognitive reflection in students to consider the importance and use of these representations for their learning of chemistry. Students understood and could elaborate the meaning of triangulation. However, their views of the importance and reported use of cognitive processes associated with it varied across individuals. Despite the variation, this study highlights the potential of developing students' metacognition by explicitly engaging them in considering means of representing the chemistry subject material they are being asked to learn, and how they might learn it using strategies and activities that are aligned with the nature of that material.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".