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Record W4230476598 · doi:10.24124/2008/bpgub1372

Adolescent perception of fun in learning decision making skills

2008· dissertation· en· W4230476598 on OpenAlexaff
Donna Stanyer

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRelevance (law)PsychologyPerceptionRecallDecision-makingMaking-ofMathematics educationDecision-making modelsClinical decision makingMedical educationSocial psychologyEngineeringCognitive psychologyMedicineAdvertisingPolitical science

Abstract

fetched live from OpenAlex

This qualitative research project examined the relevance of fun, as perceived by thirteen and fourteen year-old male and female grade eight students, in learning about decision making. The students were taught about decision making using three methods: direct teaching of the 'look, think, decide' model for decision making the 18-disc decision making game and through role play. A questionnaire was used to gather information about whether the students were more likely to recall and use a decision making process if it was experienced as fun. The 'look, think, decide' model was identified as the most effective method in helping adolscents learn about making decisions. The majority of students selected learning about decision making through role play as being most fun. Information collected suggests that fun is not the most relevant factor to consider when choosing the most effective strategy for teaching grade eight adolescent students about decision making. --P. iii.

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)0.007
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.003
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.332
Teacher spread0.318 · 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 designObservational
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

Citations0
Published2008
Admission routes1
Has abstractyes

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