A multi-user tabletop display with enhanced mobile visuals for teaching and collaborative training: faculty poster abstract
Bibliographic record
Abstract
Advances in technology provide access to cost-effective user interfaces that change the way people interact and carry out their daily activities. Massive use of smartphones, tablets, and other portable computing devices is reshaping the world of learning. Novel educational tools provide means to visualize and interact with compelling media, creating a virtual and augmented reality that can greatly enhance the knowledge transfer [5]. Education is also benefiting from various collaborative scenarios where students work together to overcome challenges while improving cognitive and social skills [3]. Indeed, as a way to improve learning, Information and Communication Technology (ICT) has already become an indispensable component of modern educational systems [2]. ICT brings a wealth of online features such as short messaging and chats, forums and groups, IP voice and video calls, cloud-based interactions and shared storage, etc., but it also forces students to focus on the screens of their computing devices and use the content to perform instruction-bound and other tasks. Therefore, despite all benefits that ICT brings to education, it could also have a negative impact on the learning process as it might restrict natural interactions thus isolating students and limiting their experience [1]. Here, we investigate the effectiveness of using heterogeneous computing paradigms (mobile devices and tabletop computers) in a collaborative learning environment.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.059 | 0.006 |
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".