Mobile resources to improve oral proficiency beyond the classroom: Focus on Tourism students in Central America
Bibliographic record
Abstract
Adopting a social interactionist approach to technology for speaking development (Egbert & Shahrokni, 2018), this paper evaluates the potential of mobile-based resources to promote semi-autonomous speaking practice for second language (L2) tourism students at a university in Central America. Tools were evaluated based on Chapelle and Jamieson's (2008) and Stockwell and Hubbard's (2013) criteria for evaluating technologies for L2 learning. Based on the literature reviewed and analysed and the feedback received from potential participants, we conclude that the oral presentation tasks using the virtual reality tool Google Arts and Culture Virtual Field Trips (formerly Google Expeditions), together with the pedagogical use of speech technologies, have the potential to improve speaking in mobile contexts outside the classroom. Based on these findings, this paper outlines a proposed study in which students from the target population use Google Arts and Culture Virtual Field Trips together with oral feedback from peers, text-to-speech synthesis and automatic speech recognition applications. Insights from our findings may interest L2 educators seeking to improve their students’ communicative abilities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".