An educational visual prototyping environment for real-time imaging
Bibliographic record
Abstract
This paper presents the results of a comparison study using the Visual VIPERS interface, a graphical interface which can be applied as an educational tool for novice computer vision students. The goal of the study was to evaluate the change in usability of the interface after the addition of a monitor tool, which can be used to view intermediate image results at specific stages of an algorithm. A user study was conducted in which participants were asked to find an error in a pre-assembled algorithm. Results indicate that participants using the older version of the interface (with no monitor tool) took, on average, less time to find the error than participants who used the monitor tool. However, interview responses indicated a greater level of understanding of the algorithm from participants who used the monitor tool. Interview responses also demonstrated a clear desire from users of the old interface for the addition of a debugging tool (such as the newly introduced monitor tool). Our belief is that participants using the monitor tool performed a more thorough search of the algorithm, and thus gained a greater understanding of how the algorithm operated, while attempting to determine the source of the error.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".