10 Digital Language Immersion (DLI) and Virtual Exchanges
Bibliographic record
Abstract
Types of Resources and CharacteristicsThis chapter presents a digital language immersion and virtual exchange project focused on activating sociolinguistic competence, as well as the development of students' intercultural practices.The work reflects on the efforts and initiatives carried out by the international educational community and the language teaching sector.Specifically, these initiatives involve the design of digital scenarios that offer language immersion teaching and learning experiences similar to those that take place in face-to-face settings.Therefore, we have focused on the acquisition of sociolinguistic and intercultural competence as a key pillar for determining advanced language use, given its natural association with language immersion experiences (Geeslin & Long, 2014: 202).This is because sociolinguistics in particular, as a knowledge and skill dimension for communication, can become 'a space and means of access to the language in its living state' (Fuertes Gutiérrez et al., 2021: 105).Therefore, this case study critically connects with the processes of acquiring sociolinguistic and intercultural competence in Spanish as a FL/L2 through resources designed in virtual learning environments.In turn, these are enriched with pedagogical interventions following an approach oriented towards action and interaction among learners to foster telecollaboration and production in transmedia formats.The activation and development of sociolinguistic competence in the context of second or foreign language learning is one of the indicators signaling learners' first steps as independent users of their target language.As per the Common European Framework of Reference for Languages (CEFR) (Council of Europe, 2020), sociolinguistic competence includes, among other aspects, the knowledge and skills necessary to address the social dimension of language use, as well as the lexical, phonetic and
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
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".