Open educational resources or closed learning management systems? –The Challenge of Designing ICT Support for Learning Communities in Higher Education
Bibliographic record
Abstract
ICT support for learning communities in higher education has become commonplace over the last decade. Within universities, there are usually various bottom-up initiatives, using different tools and educational designs to support learning and teaching. As this often takes place with little or no strategic approach to sustainability, at a given point universities strive for an “e-learning strategy”: They try to streamline the various initiatives and to endorse a learning management system that works across faculties. But how to design a strategy and sustainable support services that embrace the expectations of all stakeholders? To add complexity to this design process, there is not only a broad selection of learning management systems available for higher education, but also a strong movement pushing for web 2.0 applications and open educational resources instead of closed learning management systems. Thus – often incommensurate - expectations fly high from all sides. This situation forms the backdrop of the investigation at hand: The paper describes and analyses a participatory planning and implementation process of an e-learning strategy and implementation system at a higher education institute. It focuses on expectations that arise and have to be dealt with - both expected and unexpected ones, with a special emphasis on the inherent tension between implementing a closed learning management system and designing for open educational resources. Furthermore, it looks at unanticipated alliances and at unexpected turns in the development process. Results can inform similar design processes that need to resolve the inherent contradictions between open and closed systems as well as top-down and bottom-up approaches to changes of a learning culture.
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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 | 0.020 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.025 | 0.036 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".