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Record W149771380

Education Unplugged: Students Sound off about What Helps Them Learn.

2005· article· en· W149771380 on OpenAlexaboutno aff
Donaleen Saul

Bibliographic record

VenueEducation Canada · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous and Place-Based Education
Canadian institutionsnot available
Fundersnot available
KeywordsNothingCLARITYClothingClass (philosophy)SisterVisual artsElement (criminal law)Media studiesSociologyPsychologyPedagogyMathematics educationArtLawComputer sciencePolitical scienceArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

DONALEEN SAUL It was after school on a Friday afternoon at the end of a long week, and Christmas was days away. But nothing was going to stop a group of students at Vancouver’s King George Secondary School from saying what was on their minds. Sitting seminar-style under fluorescent lights in hard chairs around two pushed-together classroom tables, they were asked to talk about what helps them learn and what does not. The answers detonated from them at a speed next to impossible to keep up with, but nonetheless each student’s unique point of view came through with blazing clarity. Being challenged is the essential element for Ioana Bercea, a Grade 10 student who started her school years in Rumania, where she says the education system is much more rigorous than it is here. According to Ioana, “It may be fear-based but kids learn more.” Ioana is an avid reader, a former violin player, and an active volunteer at My Sister’s Closet, a second-hand clothing store that provides free clothes to clients of Battered Women’s Support Services. Although she is an honours student, Ioana doesn’t find school that interesting, claiming it offers few opportunities “to think outside the box.” For Tamara Mihic, the most important aid to learning is being free to speak what’s on her mind. At the top of her Grade 9 class, Tamara plays volleyball and basketball, holds down a part time job as a clerk at an adult education centre, serves as student council treasurer, and plays piano at a Grade 8 Royal Conservatory level. Although not at all reticent to say what she thinks, Tamara laments, “Lots of kids hold back. They’re too shy, they’re scared.” Conor Mervyn is a Grade 12 student in King George’s City School, a mini-school program he describes as “enriched, which means more work.” Conor is a believer in “non-coercive learning”, meaning that learning is most effective when it is self-motivated. Although a good student in his academic subjects, Conor’s passion is music. He plays the guitar and hopes to attend the music program at Nelson BC’s Selkirk College when he graduates. Of his newly acquired iPod, he says, “It just completes me.” Grade 11 student, Zlatina Radomirova, has only lived in Vancouver for two years, having previously attended school in South Africa and Bulgaria. Fond of jogging, drawing, and reading about Ancient Egypt, Zlatina notices a difference in Canadian students’ attitude toward others, compared to what she experienced in South Africa. Convinced that learning occurs best in a positive environment, she says, “The kids here don’t really respect the teachers or their peers... We can’t learn in that kind of atmosphere.” Sarika Narinesingh, a Grade 12 student, is on the honour roll and works hard to stay there. Literature is her favourite subject, she enjoys movies and art, and she hopes to get into the Emily Carr Institute’s Communication Design program after graduation. Describing a school Career Preparation course in which she had to teach a unit on Energy to Grade 7 students at Vancouver’s Space Centre, Sarika says she learns best when she is able to experience and directly apply what she is learning.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.995
Threshold uncertainty score0.410

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0070.003
Scholarly communication0.0070.006
Open science0.0010.007
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.1220.056

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 head0.014
GPT teacher head0.313
Teacher spread0.298 · 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

Citations13
Published2005
Admission routes1
Has abstractyes

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