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Record W2797961906

Open Educational Practices: Equity, Achievement, and Pedagogical Innovation

2018· article· en· W2797961906 on OpenAlexaboutno aff
Rajiv S. Jhangiani

Bibliographic record

VenueVTechWorks (Virginia Tech) · 2018
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsnot available
Fundersnot available
KeywordsEquity (law)Open educational resourcesBusinessPedagogyMathematics educationPolitical scienceSociologyPsychology
DOInot available

Abstract

fetched live from OpenAlex

Open Education practices (OEP) have emerged as a transformational force in higher education. Whereas, higher education promises to be an instrument for economic and social mobility, in reality our institutions reinforce existing inequalities: Achievement, engagement, and persistence are closely tied to affordability. Our claim to be student-centered is likewise hypocritical as faculty pressures, accreditation requirements, and budgetary constraints influence or dictate the structure and content of learning experiences. \n \nOpen Educational practices support teaching, learning, and publication in an increasingly diverse faculty and student body. OEP encompass the creation, adaptation, and adoption of open educational resources, open course development, and even the design of renewable, real-world assignments where students are empowered as co-creators of knowledge. These practices leverage learning beyond socio-economic disparities and put engaged, active student (and faculty) learning at the center. These practices champion academic freedom, pedagogical innovation, applied approaches, and innovation. OEP represents learner-centered and learning-together approaches to education that radically enhance both agency and access. \n \nThis presentation draws on a diverse set of examples to make a case for why the shift away from traditional (closed) practices is not only desirable but also inevitable, and how OEP support the modern university’s mission by serving academic achievement, faculty and student engagement, diversity & inclusion, pedagogical innovation, and the university’s Land-grant mission. \n \nThis event was part of Virginia Tech’s Open Education Week 2018 Symposium. \n \nPresenter: Dr. Rajiv Jhangiani https://thatpsychprof.com/about \n \nRajiv Jhangiani is Special Advisor to the Provost and a faculty member in the Psychology Department at Kwantlen Polytechnic University, British Columbia. He earned his Ph.D. in Social & Personality Psychology in 2009 from the University of British Columbia and has published articles and chapters in political psychology, the scholarship of teaching & learning, and open educational practices. The most recent of his two books is Open: The philosophy and practices that are revolutionizing education and science published in 2017. He is also the author of two open textbooks and editor of a third in psychology. \n \nDr. Jhangiani also serves as an Ambassador for the Center for Open Science in Charlottesville, VA, Senior Open Education Research & Advocacy Fellow with BCcampus, British Columbia, and is an Associate Editor of Psychology Learning and Teaching. He previously served as an OER Research Fellow with the Open Education Group, a Faculty Fellow with the BC Open Textbook Project, a Faculty Workshop Facilitator with the Open Textbook Network, and the Associate Editor of NOBA Psychology. \n \nHe is a well known and highly regarded expert, dynamic speaker, consultant and strong advocate of diversity and inclusion in academics, open educational practices, and the scholarship of teaching and learning across Canada and the United States.

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)0.015
metaresearch head score (Gemma)0.017
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.998
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0080.054
Scholarly communication0.0280.020
Open science0.0020.030
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0060.001

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 head0.195
GPT teacher head0.455
Teacher spread0.260 · 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
GenreEmpirical

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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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