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Record W1902821850

Facilitating Mathematics Problem-Solving Skills via Computer

2001· article· en· W1902821850 on OpenAlexaboutno aff
Doe Hee Ahn

Bibliographic record

VenueThe International Journal of Creativity and Problem Solving · 2001
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsCTBSMathematics educationTest (biology)PsychologyControl (management)Measure (data warehouse)Computer scienceArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

This study, utilizing two types of computer-based instruction (CBI) programs developed by the researcher, was to examine the effects of explicit strategies instruction on 105 sixth-grade students' mathematics problem-solving skills. Students drawn from five public elementary schools were divided into three groups (experimental, CBI control, and traditional control) and assessed on their mathematics problem-solving achievement levels (high and low) using a mathematics problem-solving sub-test from the Canadian Test of Basic Skills (CTBS-MPS) (multi-level edition. r =97). The students were given the Mathematics Problem-Solving Test which was developed for this research as well as the CTBS-MPS to measure their mathematics problem-solving performance. In order to measure their estimation of performance on the MPST, Self-Prediction of Performance, Self-Evaluation of Performance, Accuracy of Self-Prediction of Performance, and Accuracy of Self-Evaluation of Performance were measured. The findings indicated that students in the experimental group who were exposed to explicit strategies instruction via computer showed a significantly greater improvement on the CTBS than those in the CEI control or traditional control groups. However, there were no significant differences observed on the MPST, SPP, SEP, ASPP, and ASEP across the three groups.

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)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.731
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
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 integrity0.0000.001
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 head0.034
GPT teacher head0.356
Teacher spread0.322 · 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 designOther design
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
Published2001
Admission routes1
Has abstractyes

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