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Transformational Learning Theory and Service-Learning Projects

2020· book-chapter· en· W3009084153 on OpenAlexaffabout
Gillian Kornacki

Bibliographic record

VenueAdvances in educational marketing, administration, and leadership book series · 2020
Typebook-chapter
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsCourseworkChristian ministryTransformational leadershipPedagogyFocus groupWindsorPsychologyMedical educationService-learningPerceptionValue (mathematics)Mathematics educationSociologyMedicinePolitical scienceSocial psychologyComputer science

Abstract

fetched live from OpenAlex

This chapter investigates the University of Windsor's service-learning program Leadership Experience for Academic Direction's (L.E.A.D.) impact on teacher candidates' perceptions of teaching in-risk students. The L.E.A.D. program focuses on introducing teacher candidates to the Ministry of Ontario's Student Success initiatives and reflective teaching practices, and places teacher candidates with Student Success Teachers, allowing teacher candidates to learn from in-risk youth. This study adopted a qualitative approach using Interpretative Phenomenological Analysis (IPA) to examine the lived experience of graduates of the L.E.A.D. program. Five graduates of the L.E.A.D. program who are currently practicing secondary teachers in southwestern Ontario were interviewed in one focus group and one individual interview. The responses indicated themes of the importance of relationship building with students, the value of school support systems, the positive impact of L.E.A.D. coursework, and altered efficacy and perceptions of teaching in-risk youth.

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)0.003
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: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0050.026
Scholarly communication0.0070.006
Open science0.0020.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0160.002

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 head0.051
GPT teacher head0.297
Teacher spread0.246 · 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
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
Published2020
Admission routes2
Has abstractyes

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