Supporting professional learning in 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
<p>Professional learning, combining formal and on the job learning, is important for the development and maintenance of expertise in the modern workplace. To integrate formal and informal learning, professionals have to have good self-regulatory ability. Formal learning opportunities are opening up through massive open online courses (MOOCs), providing free and flexible access to formal education for millions of learners worldwide. MOOCs present a potentially useful mechanism for supporting and enabling professional learning, allowing opportunities to link formal and informal learning. However, there is limited understanding of their effectiveness as professional learning environments. Using self-regulated learning as a theoretical base, this study investigated the learning behaviours of health professionals within Fundamentals of Clinical Trials, a MOOC offered by edX. Thirty-five semi-structured interviews were conducted and analysed to explore how the design of this MOOC supported professional learning to occur. The study highlights a mismatch between learning intentions and learning behaviour of professional learners in this course. While the learners are motivated to participate by specific role challenges, their learning effort is ultimately focused on completing course tasks and assignments. The study found little evidence of professional learners routinely relating the course content to their job role or work tasks, and little impact of the course on practice. This study adds to the overall understanding of learning in MOOCs and provides additional empirical data to a nascent research field. The findings provide an insight into how professional learning could be integrated with formal, online learning. </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 | 0.013 | 0.006 |
| 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 | 0.003 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| 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