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Cultures and Self-Directed Learning

2014· book-chapter· en· W2500455555 on OpenAlexaff
Victor X. Wang, Patricia Cranton

Bibliographic record

VenueAdvances in educational technologies and instructional design book series · 2014
Typebook-chapter
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsEthnocentrismAutodidacticismGlobalizationPsychologyTest (biology)CognitionMathematics educationSociologySocial psychologyPolitical scienceBiologyEcology

Abstract

fetched live from OpenAlex

Although self-directed learning as a theory has been popularized in Western cultures, especially North America, it is in Confucius-Heritage Societies (CHS) that it has been well implemented. Western scholars who are exploring learning modes in different cultures have become interested in how the Chinese self-practice or self-learning has contributed more to high cognitive skills or higher test scores than other factors such as teaching to tests or teacher-centered instruction as heavily emphasized by educators and parents in CHS countries. Rather than teaching our scholars/readers Chinese characters such as Zi Xi, ??, these two characters imply a Western learning mode that leads to good learning if used positively and effectively in any culture. As globalization brings different cultures together, there is a great deal that we can learn from other cultures. We should guard against “ethnocentrism” if we are to promote good learning in our schools. This is explored in this chapter.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0050.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.012
GPT teacher head0.290
Teacher spread0.277 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations2
Published2014
Admission routes1
Has abstractyes

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