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Record W4389399701 · doi:10.19173/irrodl.v24i4.7356

SCOPE of Open Education: A New Framework for Research

2023· article· en· W4389399701 on OpenAlexvenueno aff
Virginia Clinton‐Lisell, Jasmine Roberts-Crews, Lindsey Gwozdz

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2023
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsnot available
Fundersnot available
KeywordsScope (computer science)Open educationScholarshipSocial justiceSociologyEngineering ethicsEducational researchOpen researchOpen learningPedagogyPolitical scienceComputer scienceTeaching methodSocial scienceCooperative learningEngineering

Abstract

fetched live from OpenAlex

The field of open education and research on the topic has notably expanded since the introduction of the term 20 years ago. Given these developments, a framework to structure research inquiry is necessary to ground and organize findings in open education. We propose the SCOPE framework for open education research: social justice, cost, outcomes, perceptions, and engagement. In this article, we explain how this framework emphasizes the need for social justice at the forefront of open education research. In addition, we incorporate existing theories in social justice, motivation, cognition, pedagogy, and engagement into each of the components to propose theoretical connections to future open education research. We suggest areas in which future research is needed. Finally, we conclude with suggestions as to how the SCOPE framework may be useful when connecting open education to open science and open scholarship as well as a call for considering intersectionality and critical methods in quantitative research (QuantCrit) in future inquiry.

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 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)0.018
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Open 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 score0.788
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0060.004
Research integrity0.0000.001
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 head0.275
GPT teacher head0.570
Teacher spread0.295 · 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 teacher head, 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".

Quick stats

Citations11
Published2023
Admission routes1
Has abstractyes

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