Identifying Middlewares for Mashup Personal Learning Environments
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 common understanding of e-learning has shifted over the last decade from the traditional learning objects portals to learning paradigms that enforces constructivism, discovery learning and social collaboration. Such type of learning takes place outside the formal academic settings (e.g., seminars or lectures) where a learning environment is created by using some kind of web application mashup tools. The use of these mashup tools moves the learning environment further away from being a monolithic platform towards providing an open set of learning tools, an unrestricted number of actors, and an open corpus of artifacts, either pre-existing or created by the learning process – freely combinable and utilizable by learners within their learning activities. However, collaboration, mashup and contextualization can only be supported through services, which can be created and modified dynamically based on middlewares to suit the current needs and situations of learners. This article identifies middlewares suitable for creating effective personal learning environment based on Web 2.0 mashup tools. This article also proposed a general framework for constructing such personal learning environments based on Ambient Learning realized by learning agents and the use of Enterprise Mashup servers.
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 | 0.000 | 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 | 0.001 | 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