Augmented Reality Mobile Application Co-Design Experience: Delineating the Nuances from the Lens of a System Developer
Bibliographic record
Abstract
With the rapid development of educational tools, co-design has been on the rise. Co-design is instrumental in successful product development as it merges two key perspectives, namely consumers’ insights and professionals’ knowledge. The roles of users, designers, and developers are now blurred as educators have begun to construct their own tools for teaching based on problems and ideas conceived in their classrooms, thereby assuming roles as designers and to some extent, as developers. A more common practice in recent days is educators’ co-designing of tools with system developers to achieve their targeted goal. This study attempted to explore the process of co-designing an augmented reality (AR) mobile application and the role of a system developer in mediating the design process with non-designers, namely academic members of the Faculty of Medicine and Health Sciences, Universiti Putra Malaysia. This study further aimed to delineate the nuances of the AR mobile application co-design experience from the perspective of the system developer. Semi-structured interviews, observations, and document analyses were conducted to examine the detailed process of co-designing and developing the AR application as well as to understand the developer’s interaction with the consumers. The main findings of this study suggest that the waterfall model of the Software Development Life Cycle was in use during the co-design process. This cycle generally involves five stages, namely (i) planning, (ii) analysis, (iii) design, (iv) implementation, and (v) maintenance, which are iterative in nature. The designer’s role in mediating the co-design process encompassed balancing their knowledge and experience with the needs of consumers that do not necessarily match the designer’s expectations. This was achieved through (i) precise communication, (ii) commitment to the delivery and quality of the AR mobile application, as well as to building cohesive working relationships, and (iii) motivation to work with co-designers during the development process. The findings shed light on the value of co-design and the complex role of designers in mediating the design process with non-designers, which when accounted for, can lead to more feasible project development.
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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".