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Record W2769127449 · doi:10.19173/irrodl.v18i7.3541

Special Report on the Role of Open Educational Resources in Supporting the Sustainable Development Goal 4: Quality Education Challenges and Opportunities

2017· article· en· W2769127449 on OpenAlexafffundvenueabout
Rory McGreal

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2017
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsAthabasca University
FundersMinistry of Advanced EducationAthabasca University
KeywordsOpen educational resourcesOpen educationSustainable developmentQuality (philosophy)Educational technologyGoal settingGoal orientationKnowledge managementEducational resourcesDistance educationComputer scienceProcess managementPsychologyPedagogyBusinessPolitical science

Abstract

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Quality Education: Role/Contribution of OEROpen Educational Resources (OER) and their offspring, Massive Open Online Courses (MOOCs), are becoming important factors in achieving the Sustainable Development Goal 4: Quality Education (SDG4).This was recognised early on by UNESCO in 2004 when they first coined the term "Open Educational Resources" and in 2012 with the OER Paris Declaration.UNESCO is continuing its support of OER with the 2 nd OER Summit in September 2017.There is recognition that OER and MOOCs, while not being the solution to the world's educational crises, will play an important, if not essential, role.The OER movement is less than 15 years old and is growing rapidly as more and more nations and institutions adopt the view that publicly-funded research and educational content belongs to the people and should therefore be open and accessible to them.Canada can play an important role in supporting SDG4 by increasing its support for OER and open education in general, both in Canada and abroad.It is reasonable to argue that OER can greatly benefit developing countries, even more so than those that are considered developed.As such, they can play an important transformative role in supporting the SDG4 goal.OER can be used to increase access to learning for those living in unfavourable circumstances, and can also be used to address issues of cost, quality, and equity.They are free of licensing restrictions that inhibit quality improvements and because they are openly available, OER can facilitate both internal and external collaborations among instructors and institutions, both locally and internationally, while ensuring equitable access to knowledge and learning.OER are presently being actively used in all sectors of education including literacy, vocational, and adult education, as well as with immigrants and refugees (with prior learning assessment and recognition).OER can be instrumental in improving the quality of Special Report on the Role of Open Educational Resources in Supporting the Sustainable Development Goal 4 McGreal 293 education through open curriculum development.And, most importantly, OER are digital; the world economy is digital and students must learn how to work (and play) in digital environments.Because OER can be reproduced at virtually no cost, they can be effectively used to reach vast numbers of learners, while supporting quality enhancements.As well, since they can be reused, revised, remixed, redistributed, and retained, OER can be adapted to different learning environments (Wiley, 2014).It was UNESCO that first used the term open educational resources, defining them as "teaching, learning or research materials that are in the public domain or released with an intellectual property license that allows for free use, adaptation, and distribution" (UNESCO, 2002, par.2).The UNESCO Paris Declaration of 2012 followed, supporting OER for development and recommending that educational materials developed with public funds be made available under open licenses (UNESCO, 2012).Additionally, because OER are open, they can be translated into different languages and localised to meet the needs of different countries, regions, institutions, and learners (Butcher, Kanwar, & Uvalic-Trubic, 2015).OER, as content, are free of cost to learners and teachers.However, there are other costs that must be taken into account, including the salaries and time of the OER creators/adapters/assemblers.These costs can be significantly reduced by sharing OER with other institutions.For example, an institution, rather than creating its own course material, can choose to make use of OER that have been created by others, thus saving the cost of content creation; however, there still may be costs for searching/finding, assembling, and adapting the content.In addition, an appropriate technological infrastructure needs to be in place with access to adequate bandwidth and wifi, as well as trained support staff.(Please note that these costs are the same when using commercial content.)Furthermore, the cost of commercial textbooks is becoming a greater problem as the prices are all too often increasing beyond the ability of students to pay for them.As a result, there has been rapid growth in the number of learners internationally, who cannot afford the high cost of textbooks.The advantage of OER is that they are cost-free and can be adapted freely.The ability to adapt and modify content is an important advantage, especially when targeting specific populations such as those with disabilities or special needs. Indigenous KnowledgeOER can also be used to preserve and distribute Indigenous knowledge, which is being supported now in many countries.OER can be used to support the "participatory principle" that is common in many Indigenous communities, as well as preserving and distributing Indigenous knowledge that has traditionally been open.Indigenous knowledge is seen as belonging to the community as a whole and like OER, it can be continually enhanced and expressed in many forms such as in stories, dance, songs, and through the wisdom of elders.Sharing is conducted through personal interactions among the community/kinship or religious groups (Bertini, 2010). Maskwacis First Nation Cultural CollegeAs a Canadian example, in Alberta, the Chair has worked with this college to introduce them to OER.They received funding from the Ministry of Advanced Education for the development of Indigenous content

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 imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.997
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0020.001
Scholarly communication0.0060.005
Open science0.0030.006
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0300.010

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 head0.179
GPT teacher head0.475
Teacher spread0.296 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations93
Published2017
Admission routes4
Has abstractyes

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