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Record W3127106702 · doi:10.20381/ruor-25972

Paying Lip Service to Education: An investigation of Teacher Candidates' Perceptions of 21st Century Learning

2021· dissertation· en· W3127106702 on OpenAlexfundaboutno aff
Catherine E. James

Bibliographic record

VenueuO Research (University of Ottawa) · 2021
Typedissertation
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsnot available
FundersUniversity of Ottawa
KeywordsPerceptionService-learningService (business)PedagogyPsychologyMathematics educationMedical educationMedicineBusinessMarketing

Abstract

fetched live from OpenAlex

This thesis is a study of beginning teachers’ concept of self at the precarious juncture between teacher education and a career in the classroom. Given that the practice of teaching sits at the juncture of past methods and proposed “new” models for teaching and learning, the ways preservice teachers take up the history of teaching in Ontario, their past learning experiences, and personal memories was of significance in understanding how they see themselves as future educators. Situated within curriculum studies, and using currere (Pinar, 1975) as a methodology, this inquiry focuses on the experiences of four Bachelor of Education students at a university in Ontario, Canada. Interviews were structured around the four stages of currere (regressive, progressive, analytic, and synthetic) and sought to unpack how the autobiographical free-associative utterances of teacher candidates might be read with and against the larger context of “21st century teaching and learning” competencies published by the Ontario Ministry of Education which are meant to guide their careers. Using rhetorical analysis to identify the repetitions, ambivalences, metaphors, detours, free associations, and symbolizations in the interviews, the teacher candidate narratives were coded for repetitions and meanings using a psychoanalytic stylistic. Looking at the ways the unconscious performs consciously provides insight into the ways teachers take up existing societal discourses and return to childhood experiences and traumas to forge their emerging teacher identities. The findings of this study indicate that preservice teacher candidates are ambivalent about both the language and implementation strategy of 21st century competencies even as they struggle with their own personal identity formation as educators in relation to the expectations placed upon them from the Ministry. As well, the participants return, in their free associative narratives, to scenes of learning and not-learning in childhood to guide them in their understanding of what it means to be a good teacher in the present moment. Weaving memories of childhood learning with new knowledge acquired in teacher education, each participant defines 21st century learning and its necessary knowledge and appropriate emotional register differently. Th multiplicity of subjectivities that emerges lends rich insight into the differences in teacher identities even as students emerge from the same undergraduate program. Furthermore, this study lays the groundwork for future examination of 21st century learning objectives in a post-COVID-19 context and what the implications might be for new teacher identities as historical discourses of what it means to learn and succeed invariably need to change as students move out of the brick-and-mortar schoolhouse to remote and virtual 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)0.004
metaresearch head score (Gemma)0.013
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 score0.039
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0100.004
Scholarly communication0.0060.002
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.118
GPT teacher head0.413
Teacher spread0.295 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

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