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Record W2981289128 · doi:10.19173/irrodl.v20i4.4034

Open Universities: Innovative Past, Challenging Present, and Prospective Future

2019· article· en· W2981289128 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2019
Typearticle
Languageen
FieldComputer Science
TopicEducational Innovations and Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsHigher educationDistance educationCompetition (biology)RestructuringOpen educationPublic relationsOpen innovationVariety (cybernetics)LegitimacyWork (physics)SociologyPolitical scienceBusinessEngineeringMarketingPedagogyComputer science

Abstract

fetched live from OpenAlex

This article examines the innovative past of the large-scale, single-mode open universities that follow the model of the UK Open University (UKOU), analyzes the main challenges which they are currently facing in the digital era, and concludes with highlighting leading prospects for their future operation. The establishment of the UKOU in 1969 marked a new era in distance higher education. It gave distance education a new legitimacy and opened up new prospects for populations that for a variety of reasons were unable to attend a campus-based university. Many of the new open universities were heralded as a conspicuous development in higher education, with innovative features such as: open access, reaching out to part-time adult students, providing academic faculty the opportunity to work in teams to prepare study materials, modular credit accumulation, teaching huge numbers of students, and harnessing innovative technologies into their teaching/learning processes. In the last three decades, many of these innovative characteristics pioneered by open universities have been adopted by campus universities. This has eroded the unique status of open universities in many national jurisdictions. Furthermore, the emergence of digital technologies has challenged the underlying premises of the industrial model of many open universities, as well as their logistic operation. Present challenges facing open universities emerge from: blurred boundaries between distance and campus universities; the changing of initial target populations; the need to restructure the technological and logistic infrastructure of open universities; the changing roles of the academic faculty; and the growing competition for both students and funds. In order to find success and keep being relevant in the future, open universities should take into consideration: future target populations; the use of MOOCs and OER; support systems for both students and professors; collaboration with other higher education institutions; collaboration with the corporate and work worlds; and enhancing the academic status of open universities.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.924
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.067
GPT teacher head0.425
Teacher spread0.357 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it