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Record W2933895705

Understanding Culturally Diverse Learners’ Motivation and Engagement Processes as situated in Inquiry-based Learning Context

2019· article· en· W2933895705 on OpenAlexaffabout
Aloysius C. Anyichie, Deborah L. Butler

Bibliographic record

Venue2019 Conference of the Canadian Society for the Study of Education · 2019
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSituatedStudent engagementContext (archaeology)PedagogyPsychologySituated learningMathematics educationComputer science
DOInot available

Abstract

fetched live from OpenAlex

Student learning processes, including motivation and engagement, have been identified as malleable and situated in context. We have limited understanding about how to enhance motivation and engagement for culturally diverse learners in todays’ classrooms. To support thinking about that challenge, this work built on research on both culturally responsive teaching (CRT) and self-regulated learning (SRL), each of which identifies pedagogical practices that enhance student engagement and motivation. This study examined how students at a culturally diverse public elementary school in the West Coast of Canada participated in classroom context that integrated CRT and SRL practices. Specifically, this completed pilot study explored culturally diverse learners’ engagement and motivation during an inquiry-based learning project. Data collected included classroom observations, practice records and documents, students’ work samples, teacher and student interviews and student surveys. The results demonstrated: (1) above medium levels of engagement and motivation, among participants, that varied across specific contexts; and (2) associations between culturally diverse learners’ engagement and motivation; and, inquiry-based learning context. Implications for theory, practice, and research are discussed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.126
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.226
GPT teacher head0.382
Teacher spread0.155 · 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 teacher head, 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

Citations3
Published2019
Admission routes2
Has abstractyes

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