Empirical analysis of teacher-student interaction patterns in synchronous online learning: Teaching English as a Foreign Language in Vietnam
Bibliographic record
Abstract
Synchronous online learning (SOL) is becoming a common learning modality among students in higher education. However, concerns remain about student loneliness, stress, anxiety, and social isolation arising from reduced face-to-face interaction. Students’ language learning often depends on teacher-student interaction, an important element of language acquisition. While studies examine interaction types and their frequencies, how these occur in SOL needs more focus. This exploratory study explored various interaction patterns between a university teacher and students in an online English class delivered through Microsoft Teams. Interaction transcript data were extracted from fourteen SOL sessions and analyzed using Content and Thematic Analyses. The findings reveal five interaction patterns: Moving along, Coaxing, Degrading, Demanding, and Polling. Data were further analyzed for prevalence and frequencies. Moving along was the most prominent pattern observed in the data. In this pattern, the teacher tends to progress the learning activities after observing students performing satisfactorily on a given task. Coaxing was the second frequently observed pattern. It entails the teacher encouraging interaction among students when they sense students are delaying their response to particular activities, stimulating in-depth discussion. Degrading and Demanding were the least common patterns to students’ unsatisfactory responses. Polling interaction patterns occurred fairly often when students were given time and space to respond to the teacher’s query, intended to improve engagement. The study provides a generic and practical view of interaction patterns in SOL and implications for teaching and learning in SOL environments.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".