Promoting active participation of the learners in an authoring based learning movie system
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Netflix, Hulu, etc are some of the most popular video content streaming services that are increasingly being accessed through many popular consumer devices such as Apple TV, XBox, Wii, etc. It has now become possible to conveniently interact with the video contents by using the input hardwares that these devices provide. We emulate the setups that many of these popular platforms provide in order to develop learning based video interaction games. The games leverage the user interaction feature with the video contents. In the award winning learning television series such as Mickey Mouse ClubHouse, WordWorld, Super Why etc., the protagonists present learning based questions by using various scenarios and viewers learn the answers passively as they wait. In order to foster active participation of the viewers, we author the movie with learning questions at particular timelines of the video and provide interaction options. In those specified timelines, the learners interact with the presented questions by using Wii's pointMe or XBox Kinect's gesture based interactions and input answers. The interactions assist the learners to engage with the video contents and make it possible to actively participate in the learning process. In order to examine the suitability of the proposed approach, we perform usability experiments in a technology-augmented learning space and report our findings.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it