Together… Alone in the Digital Terrain: Experiences of Interculturality in English as a Lingua Franca Virtual Exchange Among University Students in Canada and Jordan
Bibliographic record
Abstract
Virtual exchange (VE) has enabled geographically dispersed learners of English as an Additional/Second Language (EAL/ESL) to collaborate in developing intercultural competence (IC) (O’Dowd, 2011). However, recurring applications of IC constructs that overlook technology-mediated contexts and social practices common in VE restrict exploration of newly emerging practices of IC (Thorne, 2016) and the ‘simplexity’ of the VE environment (Dervin, 2016). Likewise, continued centering of the “native speaker” in VE problematizes students’ essentialist engagement with so-called “authentic cultural representations” (O’Dowd, 2021). There is therefore an urgent need for research exploring how students navigate IC in English as a lingua franca (ELF) VE environment. This study adopts an exploratory qualitative design, combining digital ethnography (Hine, 2015) with a multiple case study approach (Yin, 2014), to examine the interculturality (Dervin, 2016) experiences of EAL/ESL university students in a VE between Canada and Jordan. It investigates what experiences shape students’ strategies for engaging with IC in small groups, what factors influence their engagement, and how these experiences contribute to evolving epistemologies of IC in technology-mediated language learning. Data sources included a pre-study survey, semi-structured interviews with stimulated recall (Gass & Mackey, 2022), and observations of participants’ multimodal interactions and artifacts. Interpretive thematic analysis (Clarke et al., 2015) and multimodal discourse analysis (Kress, 2010) were used for within- and cross-case analysis. Findings show that students’ IC strategies were co-constructed through relational dynamics shaped by the VE context. Participants used storytelling, mentorship, linguistic adaptation, politeness, let-it-pass strategies, and distributed leadership to navigate tasks. These were influenced by pedagogical choices, learner identities and evolving experiences, ELF as a shared communicative ground, and the digital affordances of VE tools. While some intercultural traits aligned with conventional IC models, the findings challenge static views of cultural knowledge. Learners co-constructed diverse cultures of learning where IC emerged as fluid, emotional, and context dependent. The study contributes to VE scholarship by emphasizing a shift from individual to relational understandings of interculturality in VE and wider TMLL environments. It offers implications for designing equitable, emotionally aware VE experiences and extending IC frameworks to better reflect the complexities of digital intercultural communication.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.033 | 0.020 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".