Education as a service (EaaS): Unlocking new possibilities
Bibliographic record
Abstract
The integration of technology in education has revolutionised the sector, fundamentally transforming how knowledge is accessed, delivered and consumed. A key change is the democratisation of education, as technology dismantles barriers to learning by providing unprecedented access to information. Personalised learning experiences have been facilitated through adaptive algorithms that tailor content to individual student needs, enhancing engagement and promoting deeper understanding. This technological integration has also spurred the emergence of new players, such as EdTech start-ups and companies, which develop innovative solutions to enhance teaching and learning. These developments have shifted the provision of education from traditional institutions to include non-traditional players, giving rise to the concept of Education as a Service (EaaS). This paper explores the potential of EaaS to further contribute to higher education through a comprehensive literature review. A systematic review of literature analyses and synthesises over a hundred research studies, scholarly articles, books and other relevant sources from the years 2019–2023. The primary objectives are to understand how EaaS aligns with current trends in student and institutional needs and to anticipate its future evolution. The paper concludes that the outlook for EaaS is multifaceted, encompassing aspects of service quality, sustainability, societal impact and resilience, particularly in response to challenges such as the COVID-19 pandemic.
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.015 | 0.028 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".