Augmented Reality for Early Language Learning: A Systematic Review of Augmented Reality Application Design, Instructional Strategies, and Evaluation Outcomes
Bibliographic record
Abstract
In this article, we present a systematic review of literature on augmented reality (AR) supported for early language learning. We analyzed a total of 53 papers from 2010 to 2019 using qualitative analysis with complementary descriptive quantitative analysis. Our findings revealed three main AR learning activities: word spelling games, word knowledge activities, and location-based word activities. Our findings also uncovered five main design strategies: three-dimensional multimedia content, hands-on interaction with physical learning materials, gamification, spatial mappings, and location-based features. Several combinations of design and instructional strategies tended to be effective: Learning gains were enhanced by using three-dimensional multimedia with advanced organizers (presentation strategy) and/or using location-based content with learners’ self-exploration (discovery strategy); and motivation was enhanced by using game mechanisms with discovery strategy. We suggest that future designers of AR early language applications should move beyond these basic approaches and consider how unique benefits of AR may be applied to support key activities in early language learning while also considering how to support sociotechnical factors such as collaboration between teachers and learners and different learning contexts. We conclude with a discussion of future directions for research in this emerging space.
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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 | 0.015 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.011 |
| 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) | 0.003 | 0.001 |
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".