Openness and innovation in online higher education: a historical review of the two discourses
Bibliographic record
Abstract
This article tackles a critical question:‘to what extent can online higher education (HE) be open and innovative at the same time?’ To provide a more comprehensive answer to the question, the author takes up a notion of discourse and situates the analysis in a specific online HE setting: Athabasca University (AU), Canada. In this article, the author first unpacks how the openness and innovation discourses originally emerged in AU throughout its early years and how the original conceptualisation of the two and their relationships have shifted in more recent years. The results demonstrate that there has been an increasing level of discontinuity between the conceptualisation of openness and innovation as independent principles and the operationalisation of the two, incompatible in course design practice at AU. Being fully open to diverse student groups and being technologically innovative by integrating a state-of-the-art technology cannot be achieved in a single online course. In addition, being pedagogically innovative by increasing interactivity among students while maintaining the same level of flexibility provided by the independent study model seems very challenging. This article also discusses the institutional conditions that make teaching-oriented innovation more difficult to achieve.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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 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".