Personalized presentation of multimedia objects for home healthcare environments: a peer-based intelligent tutoring approach.
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
In this paper we present an approach for reasoning about which media content from an existing repository should be presented to users. We elaborate on our technique by considering students within an e-health intelligent tutoring environment. Our approach models the benefits in socially connected learning gained by peers in order to then recommend those objects predicted to offer the best gains in knowledge for the student. This is achieved in a framework where the past learning gains of peers are modeled and recorded with the objects in the repository. We previously confirmed the value of the approach by simulating student learning. From here, we then conduct a user study comparing the learning achieved by students presented with objects selected by our algorithms, compared to a less principled approach for curriculum sequencing; this is performed for the application of home healthcare (assisting caregivers of autistic children). We provide compelling evidence for the value of our proposed vision for achieving effective peer-based tutoring: through past experiences of peers in an extensive repository.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 |
| 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