Exploring XR Games in English Instruction: A Systematic Review of Empirical Studies
Bibliographic record
Abstract
Extended reality (XR) technologies (VR/AR/MR) have propelled a paradigm shift in the field of ESL teaching and learning. This study summarizes the evidence and development of XR games in ESL instruction in Chinese Mainland and reveals its overview and development trends and research implication. This paper presents a holistic review and analyses 17 articles published between 2013 and 2023 that are indexed in the Web of Science Core Collection. XR-powered game-based language learning was mostly applied at the tertiary education. They primarily focused on vocabulary, followed by general English, writing. Papers reviewed focused on AR than VR or MR. Concerning sample size, 8 articles reviewed reported large. The review unravels the trends, gaming characteristics and game engines of contemporary XR games. AR and VR technologies assisted language learning mainly by immersing learners in a virtual world with 3D images, videos and games. Terminal equipment, portable or mobile devices, tablets and computers are most widely used devices to augment a real environment. All the studies were identified to be self-developed to explore the design and development of XR-supported game-based learning environments. OpenSimulator, Unity 3D, ARIS, Java 1.7, Android SDK, Wikitude SDK, JigSpace, 3ds Max software are common game engines. The results reveal that integrating XR games benefited English learners in terms of cooperation, motivation, enthusiasm, engagement, immersion and presence, and the development of four English language skills and knowledge. Research recommendation for implementing XR games in ESL instruction for institutions, researchers, and educators is also discussed.
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.
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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".