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Record W2585664869 · doi:10.5539/ies.v10n2p148

Construction of High School Students’ Abstraction Levels in Understanding the Concept of Quadrilaterals

2017· article· en· W2585664869 on OpenAlexvenueno aff
Mega Teguh Budiarto, Siti Khabibah, Rini Setianingsih

Bibliographic record

VenueInternational Education Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicMathematics Education and Teaching Techniques
Canadian institutionsnot available
FundersUniversitas SurabayaKementerian Riset, Teknologi dan Pendidikan Tinggi
KeywordsAbstractionQuadrilateralMathematics educationQualitative researchRelation (database)Task (project management)PsychologyPlane (geometry)Computer scienceGeometryMathematicsEpistemologyEngineeringSociology

Abstract

fetched live from OpenAlex

The purpose of this study was to examine the abstraction thinking or the vertical reorganization activity of mathematical concepts of high school students while taking account of the abstraction that was constructed earlier, and the socio-cultural background. This study was qualitative in nature with task-based interviews as the method of collecting the data. It involved 62 high school students, and conducted for one year. The study focused on activities related to how the subjects grouped plane figures, recognized the attributes of each two plane figure, recognized the relation among them based on their attributes, defined plane figures, connected their attributes, as well as constructed the relations among plane figures. The results indicates that the abstraction level of high school students in constructing the relations among quadrilaterals consists of concrete visual level, semi-concrete visual level, semi-abstract visual level, and abstract visual level, together with indicators of each level. Therefore, the researchers suggest that it is necessary to design a learning activity that facilitates the four levels of abstraction, so that a student might increase his/her level of abstraction.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.389

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.231
GPT teacher head0.514
Teacher spread0.283 · 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 designTheoretical or conceptual
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

Citations13
Published2017
Admission routes1
Has abstractyes

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