The agency development of teachers of an additional language via immersive VR telecollaboration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Teacher agency is essential for professional growth and teachers’ ability to support transformative student learning. This study looks into the potential of virtual reality (VR) as a joint practice teaching and reflective discussion environment for achieving language teacher agency. A group of teachers of an additional language (LX) participated in a telecollaborative project. They were provided with VR devices and language learning software, Immerse, for practice teaching and reflective discussions after each VR teaching. The data of this qualitative study were video recordings of the teaching and discussion sessions and the participants’ answers to an end-of-project questionnaire based on the Cognitive Affective Model of Immersive Learning (CAMIL) by Makransky and Petersen in 2021. The video recording analysis results showed that the participants used almost all the teaching facilitation functions (e.g., tools that allowed teachers to present content, conduct classroom management, and monitor students) provided by Immerse. The themes that emerged from the participants’ reflective discussions analysis also indicated mutual support for teacher agency development. Their responses to the questionnaire revealed that taking advantage of the teaching facilitation functions reflected the factors affecting learner agency as outlined in the CAMIL model. The findings of this study provided empirical evidence that VR environments can be a viable choice for teacher intercultural collaboration in which teachers of different backgrounds and experiences could together explore the potentiality of VR for teaching an additional language, sustain one another’s growth, and achieve teacher agency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it