Context-aware Cloud-based Mobile Application for Assessment and Training of Visual Cognitive Abilities
Bibliographic record
Abstract
Context-aware mobile applications can adapt to different mobile, user and application contexts. Mobile cloud computing has been integrated with those applications to benefit from the cloud resources and make up for the limited mobile resources. This paper proposes such an application for the assessment and training of student visual cognitive abilities and skills that constitute an integral part of student intelligence such as the visualization ability of recognizing rotated objects. The need to ubiquitously and continuously deliver exercises relevant to a specific visual cognitive ability or skill according to the student proficiency and context has stimulated proposing the context-aware cloud-based MObile application for assessment and training of visual Cognitive Abilities (MOCA) presented in this paper. Integrating cloud computing with MOCA allows creating an extendible repository on the cloud and saving the relatively limited mobile resources that would be consumed by visual material. MOCA can be used in applications that are based on such cognitive abilities such as teaching visual science concepts and the visual classification and diagnosis of medical images. Two prototype mobile applications have been developed based on MOCA for visualization ability and for visual classification of science concepts. Empirical evaluation has shown the effectiveness of MOCA in training the students and the satisfaction of the students and teachers with its capabilities. MOCA is also a framework for building systems for other types of cognitive abilities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".