Learning in a small, task–oriented, connectivist MOOC: Pedagogical issues and implications for higher 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>Despite the increase in massive open online courses (MOOCs), evidence about the pedagogy of learning in MOOCs remains limited. This paper reports on an investigation into the pedagogy in one MOOC - Oxford Brookes University’s ‘First Steps in Learning and Teaching in Higher Education’ MOOC (FSLT12).</p><p>FSLT12 was an open and free professional development opportunity for people moving into HE teaching. It was a small course (200 participants registered from 24 countries) which was focused on introducing HE teaching skills, and, uniquely, to deliberately integrate open academic practice as a vital part of professional development for HE teachers. A qualitative, case-study approach was used in the research, based on surveys, interviews, and social media, to provide evidence about how people learned in this course and consider wider implications for teaching and learning in higher education.</p><p>The evidence shows that participants who completed the course were able to learn autonomously and navigate the distributed platforms and environments. The most challenging issues were acceptance of open academic practice and difficulty in establishing an academic identity in an unpredictable virtual environment. An interesting and significant feature of the course was the support for learners from a number of MOOC ‘veterans’ who served as role models and guides for less experienced MOOC learners.</p><p>The research shows that small task-oriented MOOCs can effectively support professional development of open academic practice.</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 | 0.002 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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