Characteristics of teacher talk and learner talk in the online learning environment
Bibliographic record
Abstract
The hierarchical system of speech acts developed by Sinclair and Coulthard (Towards an analysis of discourse: The English used by teachers and pupils. Oxford: Oxford University Press, 1975) to describe interaction in traditional classrooms and later applied to face-to-face communication by Stenström (An introduction to spoken interaction Series: Learning about language. New York: Longman Publishing, 1994) and asynchronous conversations by Harrison (E-mail discussions as conversation: Moves and acts in a sample from a listserv discussion. Linguistik online, 1, 1998) is used here to analyse interaction in an online graduate education context that employed a peer group discussion model. Message content was analysed to determine if there exists an online variant of teacher talk and learner talk and the extent to which participants adapted previously identified speech components and patterns. Quantitative and qualitative differences in the organisational, interactive, and content-related features of both teacher's and learners' contributions were found. Participants in asynchronous text-based interaction used a rich variety of speech acts, some of which are used in traditional classroom interaction and peer group conversations that are conducted in both face-to-face and asynchronous contexts, and others that are unique to the online educational environment.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".