Reflections on Remixing Open Access Content into Open Educational Resources: A New Paradigm for Sustainable Data-Driven Language Learning Systems Design in Higher Education
Bibliographic record
Abstract
This chapter presents a new paradigm for sustainable data-driven language learning systems design in higher education that draws on qualitative reflections spanning a decade (2012–2022) with stakeholders from an ongoing global research study with the FLAX (Flexible Language Acquisition) and F-Lingo projects at the University of Waikato in Aotearoa New Zealand (Fitzgerald (2019) A new paradigm for open data-driven language learning systems design in higher education; König et al. (2022) Smart CALL ). Design considerations are presented for remixing domain-specific open access content into Open Educational Resources (OER) for academic English language provision across formal and non-formal higher education contexts. Primary stakeholders in the research collaboration include the following three groups: (1) Knowledge organisations that provide open access to academic content—libraries and archives, including the British Library and the Oxford Text Archive, universities in collaboration with MOOC providers and the CORE (COnnecting REpositories) open access aggregation service at the UK Open University; (2) Researchers who mine and remix academic content into corpora and open data-driven language learning systems—converging from the fields of open education, computer science and applied corpus linguistics; (3) Knowledge users who re-use and remix academic content into OER—English for Academic Purposes (EAP) practitioners from university language centres. Automated content analysis was carried out on a corpus of interview and focus discussion data with the three stakeholder groups in this research. We discuss themes arising from the research data that reflect the different stakeholders’ experiences of remixing open access research content that has been produced within the academy for re-use as open educational content for teaching and learning features of academic language within open data-driven language learning systems. These open learning systems have been specifically designed to scale with OER expansion and traction in mind for their sustainable uptake both within and beyond the brick and mortar of the traditional university. The new paradigm presented in this chapter challenges, as the OER movement must, established business models and deeply embedded cultural or institutional norms that present obstacles to OER expansion and traction and the sustainability of the movement. One persistent challenge concerns the lack of open education policy across the higher education sector for full open access (for use, modification, adaptation) via Creative Commons licensing to content produced within the academy. Thus, while this research has theoretical and practical implications in applied linguistics, computer science, language teaching and learning and open education, more generally, it also has significant cultural, business model and policy implications for higher education.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Open scienceScholarly communication Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | Scholarly communicationOpen science Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | medium |
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.015 | 0.031 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".