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Record W3087693774 · doi:10.11575/prism/38210

Intellectual Emancipation and Embodiment in Early Mathematics Learning

2020· dissertation· en· W3087693774 on OpenAlexaboutno aff
Shimeng Liu

Bibliographic record

VenueOpen MIND · 2020
Typedissertation
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsEmancipationMathematics educationSociologyPedagogyMathematicsPolitical scienceLaw

Abstract

fetched live from OpenAlex

When mathematics language is defined narrowly, emergent bilinguals in classrooms could be systematically positioned as “learners of deficiency.” Recent scholarships in the field of learning sciences call for expanding the notion of mathematics language and scrutinising learning opportunities of emergent bilinguals in relation to the history and institutional spaces. Taking a holistic and critical perspective, this study draws from Rancière’s notion of intellectual emancipation as the leverage for emergent bilinguals’ agency in mathematics learning. My study was situated in a larger project conducted in a linguistically and racially diverse school in Western Canada. Together with a teacher, the research team altered temporal-spatial structure of the mathematics classroom that can mobilize learners’ bodies in an intellectually emancipatory manner. My analysis focused on classroom discourses and emergent bilinguals’ agency in different configurations of learning environment. My findings show, in the routine session, the teacher’s intelligence and will prevailed over that of students, thus the Initiation-Response-Evaluation (IRE) or Initiation-Response-Feedback (IRF) sequences were quickly completed and the discourses alternative to the pre-set plan were discouraged. The narrow space that configured the routine session also constrained the mathematics thinking mediated by bodies to a minimal level. The teacher’s monitoring of students’ physical movements further tightened the control over learner bodies. In this learning environment, the mathematical thinking and learning tended to be compressed to unidirectional acquirement. Conversely, in the designed session, the teacher’s will and students’ intelligence took the lead. Temporal structure of classroom discourse was thinned out to the expanded intervals between teacher utterance and student utterances, and even with the absence of “evaluation” in the sequence of IRE/F. The previously restrictive area in the school was transformed to a place that augments the embodied mathematics learning. Temporally and spatially, the designed sessions were expanded and offered more uncertainty and spontaneity due to the decreased control of the teacher as an explicator. In this context, mathematics pedagogy offered a complex system of iterative adaptation and decentralized learning. Based on these findings, I discuss how integrating embodied learning and the perspective of intellectual emancipation can address equity issues in early mathematics education.

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)0.003
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.016
Scholarly communication0.0050.002
Open science0.0000.006
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.100
GPT teacher head0.439
Teacher spread0.339 · 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
Published2020
Admission routes1
Has abstractyes

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