Meaning making through multiple modalities in a biology classroom: A multimodal semiotics discourse analysis
Bibliographic record
Abstract
Abstract The teaching of science is a complex process, involving the use of multiple modalities. This paper illustrates the potential of a multimodal semiotics discourse analysis framework to illuminate meaning‐making possibilities during the teaching of a science concept. A multimodal semiotics analytical framework is developed and used to (1) analyze the semiotic and epistemological meanings communicated by multiple modalities during the teaching of a biology concept and (2) highlight features of semiotic modalities that extend meaning‐making opportunities in science classrooms. The classroom discourse of a Grade 11 biology teacher was analyzed during the teaching of the concept chemosynthesis. Data were drawn from lesson transcripts, observational fieldnotes, and informal interviews with the teacher. The findings showed that the multimodal semiotics framework was useful at illustrating how semiotic and epistemological functions of modalities compounded meanings. Most significantly, an emergent multimodal framework relating semiotic functions and science learning outcomes emerged that has the potential to (1) act as a metacognitive tool for teachers to select, sequence, and scaffold modalities and (2) act as an analytical framework for educational researchers to analyze meaning making in science teaching and learning. © 2009 Wiley Periodicals, Inc. Sci Ed 94: 48–72, 2010
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| 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".