Bibliographic record
Abstract
This paper describes a cost-effective infrastructure for building ubiquitous collaborative learning spaces. It uses techniques from the Semantic Web and ubiquitous computing to build a learner-centric service-based architecture to transform existing traditional learning spaces (e.g., classrooms, computer labs, meeting rooms, and hallways) into intelligent ambient learning environments. This is achieved by blending a number of inexpensive technologies which are optimally configured to provide services that can perceive a learners' location and schedule, identify current learning activity, recommend learning resources, and enable effective real-time collaboration and resource sharing between learners and their instructors. These services are semantically defined and homogeneously integrated using a shared ontology, service policies, and inference rules. Service invocation and coordination are triggered at runtime by context-changes in the learning environment, thus offering full context awareness and providing real-time support for various learning modes, including formal, informal, and Ad hoc collaborative learning. Furthermore, the learning is supported by a range of mobile devices that are commonly used by learners to enable better instruction and communication. A prototype system is developed and tested using different learning scenarios. The system has also been tested by a group of learners whose feedback is provided for performance assessment.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".