Cloud Computing and Creativity: Learning on a Massive Open Online Course
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
This paper explores cloud computing and how it might advance learning and teaching, particularly in terms of social creativity and collaborative learning. We present a study of a Massive Open Online Course (MOOC) – a semi-autonomous learning environment mainly distributed on the cloud – in which Open Educational Resources were produced, researched and shared by participants worldwide. The objective of this research was to explore the level of importance of creativity for learning and then to closely investigate how this creativity might be fostered in such a ‘vast’ educational setting and what factors might be of importance to enhance creativity in open networked learning. Through the participants’ experiences, we discuss the various dynamics and profiles of the participants as they move from being consumers on the environment to becoming ‘producers’ and take creative steps in their learning. More importantly, we identify the elements of the course that need to be in place to encourage and support this move towards more effective creativity and learning. Finally further discussions and conclusions are presented.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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