Using mLearning and MOOCs to understand chaos, emergence, and complexity in education
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
<p>In this paper, we look at how the massive open online course (MOOC) format developed by connectivist researchers and enthusiasts can help analyze the complexity, emergence, and chaos at work in the field of education today. We do this through the prism of a MobiMOOC, a six-week course focusing on mLearning that ran from April to May 2011. MobiMOOC embraced the core MOOC components of self-organization, connectedness, openness, complexity, and the resulting chaos, and, as such, serves as an interesting paradigm for new educational orders that are currently emerging in the field. We discuss the nature of participation in MobiMOOC, the use of mobile technology and social media, and how these factors contributed to a chaotic learning environment with emerging phenomena. These emerging phenomena resulted in a transformative educational paradigm. <br /><br /></p>
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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