A Classification Framework for Context-aware Mobile Learning Systems
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
The field of context awareness is ever increasing due to the proliferation and omnipresent nature of mobile computing devices. Not only is learning becoming ubiquitous, but the sensors in mobile devices are permitting learning systems to adapt to the context of the learners. This paper provides a classification framework for the field of context-aware mobile learning, which is applied to papers published within selected journals from January 2009 to December 2015 inclusive. Obtained from the combined fields of context awareness and educational technology, a total of 2,968 papers are reviewed, resulting in 41 papers being selected for inclusion in this study. The classification framework consists of three layers: hardware architecture layer, context architecture layer and an evaluation layer. The framework will allow researchers and practitioners to quickly and accurately summarize the status of the current field of context-aware mobile learning. Furthermore, it has the potential to aid in future system development and decision making processes by showing the direction of the field as well as viable existing methods of system design and implementation.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 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