A Grounded Theory of Professional Learning in an Authentic Online Professional Development Program
Bibliographic record
Abstract
Online professional development (OPD) programs have become increasingly popular. However, participating in professional development does not always lead to profound professional learning. Previous research endeavours have often focussed on measuring user acceptance or on comparing the effectiveness of OPD with a face-to-face delivery, but there is little knowledge of how the process of professional learning actually occurs in OPD. This study explores how professional learning takes place in an OPD program designed according to the principles of authentic e-learning, and how the learning design and technologies used impact on the professional learning experienced by the participants. The context of the study is an international OPD program in vaccine management developed and offered by World Health Organization. A grounded theory approach was employed to develop a theorised model of the professional learning process in an authentic online learning environment. The findings show that professional learning was facilitated in a dynamic web of interactions rather than by covering content: the learner is at the centre of the process, actively engaged in authentic tasks in collaboration with peers, while mentors and content play a supporting role. Technology facilitates and enables the web of interactions. The learning process was found to bear resemblance to the type of professional learning that occurs in authentic workplace settings, which implies that the authentic e-learning principles provide a helpful learning design framework for OPD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".