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Record W4205229677 · doi:10.26711/007577152790063

—Examining the Impact of a Mental Computation Classroom Intervention on the Relational Thinking of Seventh-Grade Students

2021· article· en· W4205229677 on OpenAlexaff
Helena P. Osana, Alexandra N. Kindrat, Lester B. Pearson, Lester Pearson School

Bibliographic record

VenueJournal of Mathematics Education · 2021
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsConcordia University
Fundersnot available
KeywordsIntervention (counseling)Mathematics educationPsychologyPedagogy

Abstract

fetched live from OpenAlex

The present study examined the impact of a mental computation intervention on the relational thinking and equivalence knowledge of seventh-grade students. The instructional intervention focused on classroom discussions, the sharing of mental computation strategies, and conceptual scaffolding from the teacher. A multiple baseline design was used with 66 students in three classes. In each class, students were assessed at five time points on (a) mental computation, (b) mathematical equivalence problem solving, and (c) relational thinking, which was assessed by asking students to make judgments about true-false number sentences. Students in one class improved on equivalence problem solving after the intervention, although ceiling effects mitigated the observation of potential instructional benefits. All students improved their relational thinking skills, and performance paralleled observed gains in mental computation in two of the three classes. Relational thinking performance levels were maintained for up to 12 weeks. The link between mental mathematics and relational thinking implies that mental computation should play a more prominent role in the seventh-grade mathematics classroom.

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)0.005
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.641
Threshold uncertainty score0.200

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
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 head0.116
GPT teacher head0.474
Teacher spread0.358 · 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 designObservational
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

Citations2
Published2021
Admission routes1
Has abstractyes

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