Perception and Attitudes towards Augmented Reality (AR) Enhanced Academic Writing: Satisfaction Levels
Bibliographic record
Abstract
The concepts of Virtual Reality (VR) and Augmented Reality (AR) have emerged since the middle of the twentieth century. Recently, many forms of Artificial Intelligence (AI), such as virtual reality and augmented reality have become imminent in nearly all walks of life. AI has become a modern helpful tool for educators and learners of English language education. This research paper aims to investigate Arab Open University (AOU) students’ satisfaction level of using the Augmented Reality Platform (EON-XR) in learning and developing their academic writing skills as a self-learning tool. The study explores the impact of using Augmented Reality (AR) on students’ perception and attitude towards enhancing some academic writing skills. The researchers have raised some fundamental questions addressing key aspects, such as the definition of AR, the difference between AR and VR, the specific characteristics of EON XR AR platform, and the extent to which this AR platform enhances academic writing skills among AOU students. The data have been collected from a literature review spotting the need for using VR and AR applications in English Language learning especially in developing academic writing. The instrumental tools used for this study are a satisfaction questionnaire which has been adapted and developed by the researchers via reviewing relevant studies in addition to seven realistic simulations with interactive 3D models as virtual environments designed by the main researcher. The data collected from these instrumental tools have been statistically analyzed and discussed. The finding revealed that AOU students generally responded positively towards the integration of the AR platform into their learning experience to develop their academic writing skills provided that the nature of the platform is user friendly.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".