Exploring Zoom as a Platform for Language Learning: An Interactionist Approach
Bibliographic record
Abstract
Video Conferencing tools like Zoom have provided new avenues for authentic learner interaction in second language (L2) learning, supporting research that highlights the role of interaction in facilitating L2 acquisition (e.g., Putri et al., 2021; Swain, 1985). For instance, according to Long’s (1996 et seq.) Interactionist Approach, it is hypothesized that learners acquire language most effectively when they engage in meaningful communication and negotiate meaning with others. Zoom aligns with this approach by providing authentic virtual interaction opportunities (Zhao & Lai, 2023). This study draws upon Cardoso’s (2022) chronological framework for examining technological tools for L2 learning. Specifically, it focuses on stage 2 of the framework, which involves assessing the pedagogical potential of an existing technology. Following Long’s (1996) Interactive Approach, the study explored how Zoom’s features can be leveraged to promote interactions by providing learners with access to the L2 input (e.g., listening) and promoting opportunities for output (e.g., speaking) and negotiation of meaning (e.g., to solve a communication breakdown). Our analysis suggests that most Zoom features fulfil the criteria set forth by the Interaction Approach. For instance, Breakout Rooms and Polls/Quizzes have the potential to increase student engagement and provide immediate feedback. Students can also collaborate in Video Conferencing and Chats within Breakout Rooms to engage in meaningful exchanges that drive L2 learning. Our discussion of these analyses highlights Zoom’s potential as a versatile platform for language learning, offering significant benefits for both synchronous and asynchronous L2 learning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".