Dimensions of Openness: Beyond the Course as an Open Format in Online Education
Bibliographic record
Abstract
The objective of the paper is to provide a framework for understanding the pedagogical opportunities of openness in education. The paper will argue that openness in education should not only be viewed as opening existing resources and courses to a broader audience. Openness is also a matter of providing insight and enable communication and collaboration across traditional barriers – such as distance and accessibility. From this perspective, openness is the removal of barriers for interaction and exchange – and not only a matter of providing access to resources or courses. Rather, the objective is to open education to the outside world, which entails an interaction between educational institutions and society. The key point of the paper is that to do this, educational activities need to change and move beyond the course as the main unit of openness. Openness is not only a matter of opening up the existing, but to develop new educational practices that interact with the world. The paper outlines three different dimensions of openness that describe different types of interaction between institutions and society: transparency, communication and engagement. To exemplify the dimensions, the paper presents a case study that demonstrates the three dimensions of openness in an university programme. The paper concludes in a discussion of educational technologies for the different dimensions of openness.
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.
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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.041 |
| Scholarly communication | 0.020 | 0.035 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".