Marine XR: The impact of an immersive learning AR app on student motivation and engagement with the biology, ecology and conservation of basking sharks
Bibliographic record
Abstract
There is growing evidence that technology-enhanced teaching can foster engagement in scientific literacy for all students.For example, immersive educational technologies, such as augmented reality (AR), focus on engaging students by providing interactive experiences that intrinsically motivate them to explore both virtual and real environments for science learning.We developed a "tap-to-place" highly immersive augmented reality application, Marine XR, that uses the principles of gamification, simulation, role-playing and immersion to engage students in scientific concepts.Marine XR focuses on one of the world's ocean giants, the basking shark, to teach students fundamental scientific skills, while simultaneously emphasizing the importance of ocean conservation.We conducted a controlled experimental study comparing the impact of Marine XR to a more traditional webbased learning module in a large, first-year environmental sciences class under remote learning conditions (~200 students).Specifically, we measured how motivation, engagement, engrossment, and cognitive load differed between the two groups within the context of their attitudes towards science (as assessed by the Modified Attitudes Towards Science instrument).In addition, we investigated whether Marine XR could increase motivation to participate in a subsequent learning experience.The results of the study and its consequences will be discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".