Understanding Innovation Vectors in the Use of Open Educational Resources
Bibliographic record
Abstract
Open educational resources (OER) are teaching and learning materials that are either in the public domain or published on an open licence which permits various forms of redistribution, reuse and repurposing. Many organisations and higher education institutions around the world are using such resources, and anecdotally many believe this is supporting innovations in practice. However, there is scant research into how such innovations should be understood or evaluated conceptually. In this paper, we present a conceptual framework that can describe and evaluate innovative practice as well as results from a study of 44 cases using this framework in the context of the ENCORE+ (European Network for Catalysing Open Resources in Education) project (2021–2023). This conceptual framework provides a rich qualitative description for instances of innovation which use OER. Our examples cover many countries, including Argentina, Australia, Canada, China, Colombia, UK, Germany, Greece, Hungary, India, Ireland, Kenya, the Netherlands, Norway, Scotland, Slovenia, South Africa, Spain, Taiwan, USA, and Zanzibar. The sample includes organisations of all sizes and maturities of implementation. This allowed us to differentiate OER value propositions for a range of stakeholders at different levels of maturity of OER use. We explore whether variables such as the size and maturity of an organisation influences innovation strategies and the perception of stakeholder relationships. Our data indicates four elements to the development of OER value propositions as innovation vectors. Firstly, OER value propositions tend to be transformative, and focused on modifying or redefining pedagogical activity. Secondly, they are practical, targeting users/providers and influencing behaviour in direct and achievable ways. Thirdly, OER users and advocates emphasise observability, simplicity and compatibility as key aspects for communicating OER value propositions. Fourthly, OER innovation is aspirational in that greater maturity of organisations using OER sees the OER value proposition widened to include more stakeholder types.
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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".