On performing concepts during science lectures
Bibliographic record
Abstract
Abstract When lecturing, teachers make use of both verbal and nonverbal communication. What is called teaching, therefore, involves not only the words and sentences a teacher utters and writes on the board during a lesson, but also all the hands/arms gestures, body movements, and facial expressions a teacher performs in the classroom. All of these communicative modalities constitute resources that are made available to students for making sense of and learning from lectures. Yet in the literature on teaching science, these other means of communication are little investigated and understood—and, correspondingly, they are undertheorized. The purpose of this position paper is to argue for a different view of concepts in lectures: they are performed simultaneously drawing on and producing multiple resources that are different expressions of the same holistic meaning unit. To support our point, we provide examples from a database of 26 lectures in a 12th‐grade biology class, where the human body was the main topic of study. We analyze how different types of resources—including verbal and nonverbal discourse and various material artifacts—interact during lectures. We provide evidence for the unified production of these various sense‐making resources during teaching to constitute a meaning unit, and we emphasize particularly the use of gestures and body orientations inside this meaning unit. We suggest that proper analyses of meaning units need to take into account not only language and diagrams but also a lecturer's pointing and depicting gestures, body positions, and the relationships between these different modalities. Scientific knowledge (conceptions) exists in the concurrent display of all sense‐making resources, which we, following Vygotsky, understand as forming a unit (identity) of nonidentical entities. © 2006 Wiley Periodicals, Inc. Sci Ed 91:96–114, 2007
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.
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".