Analyzing the Impact of Augmented Reality on Student Motivation: A Time Series Study in Elementary Education
Bibliographic record
Abstract
Sub-optimal learning outcomes have been observed, often attributed to monotonous educational processes that struggle to retain students' focus and stimulate active participation. This study investigates the potential influence of Augmented Reality (AR) on student motivation, utilizing a time series analysis approach. The primary objectives include assessing the impact of AR on student learning outcomes and identifying the most suitable model for elucidating this relationship. The central research question is: can the implementation of AR enhance student motivation in elementary education? A time series design with a quantitative methodology was employed, involving a cohort of 29 fourthgrade students in Indonesia. Data collection was conducted through a Likert scale questionnaire. Four trend models were tested: the Linear Trend Model, Quadratic Trend Model, Growth Curve Model, and S-Curve Trend Model. The analysis of the collected data, tabulated and analyzed based on the established time series, suggests a positive correlation between AR technology implementation and student motivation. An upward trend in learning motivation was observed following the consistent application of AR technology in educational activities. Among the tested models, the Quadratic Trend Model demonstrated the least error estimate, with MAPE at 1.39, MAD at 1.08, and MSD at 1.44, suggesting it as the most suitable for further analysis related to the predictive power of student learning motivation in this context. This study advocates for the utilization of AR technology as an alternative method in classroom learning activities. The integration of learning content with game-like elements within a realistic world was observed to elicit student interest and enthusiasm. This approach is particularly recommended for educators seeking to enhance their students' learning motivation.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".