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Record W7106614234 · doi:10.55982/openpraxis.17.4.909

Student Perceptions of Open Education Practice: Navigating Privacy, Identity, and Collaboration with Participatory Technologies

2025· article· en· W7106614234 on OpenAlexaff

Bibliographic record

VenueOpen Praxis · 2025
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsTransformative learningOpen educationOpen educational resourcesEducational technologyOpenness to experienceDigital literacyCitizen journalismDistance educationTechnology integrationOpen learning

Abstract

fetched live from OpenAlex

Recent research in open education has increasingly focused on the potential of open educational practices (OEP) to foster equitable, accessible, and transformative learning experiences. This study explores students’ perceptions of openness in education, particularly within a fully online graduate program that integrates OEP. The research examines how learners navigate participatory technologies in an open pedagogy context, their shared practices and values, and the impact of open platforms on their learning experiences. Utilizing a virtual ethnographic case study approach, the findings reveal that while students appreciate the collaborative and empowering aspects of OEP, they also face challenges related to navigation, digital identity, and privacy. The study underscores the importance of critically examining the implementation of open platforms and the need for ongoing support to enhance digital literacy and effective use of open educational tools.

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)0.012
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.999
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.009
Scholarly communication0.0110.006
Open science0.0010.009
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.035
GPT teacher head0.443
Teacher spread0.408 · 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 designQualitative
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

Citations1
Published2025
Admission routes1
Has abstractyes

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