<scp>VILLAGE</scp> — <scp>V</scp> irtual <scp>I</scp> mmersive <scp>L</scp> anguage <scp>L</scp> earning and <scp>G</scp> aming <scp>E</scp> nvironment: Immersion and presence
Bibliographic record
Abstract
Abstract 3 D virtual worlds are promising for immersive learning in E nglish as a Foreign Language ( EFL ). Unlike E nglish as a Second Language ( ESL ), EFL typically takes place in the learners’ home countries, and the potential of the language is limited by geography. Although learning contexts where E nglish is spoken is important, in most EFL courses at the college level, EFL is taught by acquiring vocabularies, grammar and pragmatic features without contextual immersion. In this study, an immersive E nglish learning environment in a 3 D virtual world, O pen S imulator, was developed with two key learning artifacts, chatbot and time machine. A single‐factor, independent measures design was used to examines learners’ presence under four learning conditions: virtual learning environment without digital learning artifacts ( VE ), virtual learning environment with chatbot ( VEC ), virtual learning environment with time machine ( VETM ) and virtual learning environment with chatbot and time machine ( VECTM ). Three research questions emerging from the four learning conditions form the backbone of this study: (1) Does chatbot increase language learners’ presence in the immersive virtual E nglish learning environment? (2) Does time machine increase language learners’ presence in the immersive virtual E nglish learning environment? (3) Does the combined use of chatbot and time machine increase presence more than either learning artifact alone? The experimental results indicate that the chatbot and time machine increase the learners’ sense of immersion and presence. Best design practices should address how immersion and presence can be integrated into affordances of virtual worlds.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.047 | 0.003 |
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".