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Record W4226341237 · doi:10.1177/11771801221089685

Indigenous pedagogies and online learning environments: a massive open online course case study

2022· article· en· W4226341237 on OpenAlexafffund
Danielle Tessaro, Jean‐Paul Restoule

Bibliographic record

VenueAlterNative An International Journal of Indigenous Peoples · 2022
Typearticle
Languageen
FieldComputer Science
TopicOnline Learning and Analytics
Canadian institutionsUniversity of Victoria
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIndigenousExperiential learningRedevelopmentMassive open online coursePedagogySociologyMathematics educationPsychologyEngineeringEcologyCivil engineering

Abstract

fetched live from OpenAlex

This study is based on a massive open online course titled Aboriginal Worldviews in Education, which was created and instructed using various Indigenous pedagogies. Despite significant pedagogical differences between massive open online course and Indigenous pedagogical learning environments, this study builds the case that various Indigenous pedagogies can effectively be incorporated in a massive open online course. The study found that holistic pedagogies were effectively applied by centering course creation and instruction around Medicine Wheel teachings. The article details the various experiential and self-reflective activities that were applied to the massive open online course, and that were found to effectively address the spiritual, emotional, and physical quadrants of the Medicine Wheel that are normally overlooked in courses that stress intellectual learning. The article also suggests directions for the development and redevelopment of massive open online courses to better include Indigenous pedagogies.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.003
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.037
GPT teacher head0.368
Teacher spread0.330 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations5
Published2022
Admission routes2
Has abstractyes

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