Central Queensland University's Course Management Systems: Accelerator or brake in engaging change?
Bibliographic record
Abstract
Central Queensland University (CQU) is a highly complex institution, combining campuses in Central Queensland and distance education programs for Australian domestic students with Australian metropolitan sites for international students and a number of overseas centres, also for international students. In common with many other universities, CQU has recently reviewed its course management systems (CMSs). In doing so, CQU has signalled its desired strategic position in managing its online learning provision for the foreseeable future. This paper analyzes that strategic position from the perspective of the effectiveness of CQU’s engagement with current drivers of change. Drawing on online survey results, the authors deploy Introna’s (1996) distinction between teleological and ateleological systems to interrogate CQU’s current position on CMSs – one of its most significant enterprises – for what it reveals about whether and how CQU’s CMSs should be considered an accelerator of, or a brake on, its effective engagement with those drivers of change. The authors contend that a more thorough adoption of an ateological systems approach is likely to enhance the CMS’s status as an accelerator in engaging with such drivers. Keywords: Australia, course management systems, enterprise systems, open and distance learning, teleological and ateleological systems
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".