Implementation of a mobile peer assessment system with augmented reality in a fundamental design course
Bibliographic record
Abstract
This study proposes a framework that incorporates mobile peer assessment and augmented reality (AR) technology to enhance interaction and learning effectiveness. According to the framework, a mobile AR peer assessment system has been developed to facilitate students to improve work interpretation, frequently interact with peers, represent their thinking and reflect upon their own works anytime anywhere. Moreover, the mobile AR technology provides personalized and location-based adaptive contents that enable individual students to interact with the mixed reality environment and observe how works are possibly applied to the real world in the future. In a fundamental design course, students used the system to acquire sufficient information in indoor and outdoor situations and mark peers’ work accurately based on appropriate assessment criteria. The experimental results showed that the system really assisted students in acquiring useful information, proposing their viewpoints, and further fostering critical thinking skills and reflection. During the process of interviews, most students made positive responses and provided meaningful suggestions. The system allows students to concentrate on observing and understanding the relative explanation and representation of works within a combined real–virtual environment and apply appropriate assessment criteria that produce sufficient assessment results to mark peers’ works. Rich feedback can encourage students to reflect upon their own works and improve the quality of their works.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".