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Record W2925042501

Fostering School, University, and Community Relationships through a Family Math Night

2019· article· en· W2925042501 on OpenAlexaff
Candy Skyhar, Michael Nantais

Bibliographic record

Venue2019 Conference of the Canadian Society for the Study of Education · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Technology Integration
Canadian institutionsBrandon University
Fundersnot available
KeywordsAttendanceCurriculumNumeracyMathematics educationPedagogySession (web analytics)PsychologyMedical educationMedicinePolitical scienceLiteracy
DOInot available

Abstract

fetched live from OpenAlex

Family Math Nights, first introduced by Stenmark, Thompson and Cossey (1986), “are school-sponsored events in which parents, teachers, and students interact around a mathematics curriculum” (Lopez & Donovan, 2009, p. 220). The research project described in this paper/session sought to investigate the effectiveness of a locally organized Family Math Night that resulted through collaboration between a university faculty of education and a local school division.  The event, which was held in a local community school that had may newcomer families and students in attendance, was conceptualized and facilitated by divisional consultants, teachers from the school, university faculty members, and teacher candidates.  Evidence from the mixed methods research study indicated that the Family Math Night fostered positive relationships between school, university, and community; engaged parents and students in curriculum-based mathematics activities in a supportive, non-threatening, and fun atmosphere; provided teacher candidates with opportunities to plan for and engage with students in an authentic way; and provided parents with strategies and games they could use at home. Such findings illustrate the promise of Family Math Nights as tools not only for promoting student numeracy, but also for fostering positive school, university, and community relationships.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.501
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
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 head0.104
GPT teacher head0.318
Teacher spread0.214 · 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.

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
Published2019
Admission routes1
Has abstractyes

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