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Record W2112825066 · doi:10.5539/ies.v8n8p81

The Application of Contextual Approach in Learning Mathematics to Improve Students Motivation At SMPN 1 Kupang

2015· article· en· W2112825066 on OpenAlexvenueno aff
Ch. Krisnandari Ekowati, Muhammad Darwis, H. M. D. Pua Upa, Suradi Tahmir

Bibliographic record

VenueInternational Education Studies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicCultural and Educational Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationEnthusiasmGroup cohesivenessPsychologySocial psychology

Abstract

fetched live from OpenAlex

This research is an action research which aims to implement contextual teaching and learning (CTL) approach to learn mathematics, focus on the integration subjects. The approach utilizes the use of mathematics manipulative so that students can understand a mathematical concept to construct their own. The method which used in this research are describtive method for calculate the student results. The object of this research are 41 students of the 7thE grade students of SMPN 1 Kupang. Mathematics Teacher and researchers became observer. The research was conducted in three cycles, with an overview of the following results; (1) there is an improvement of student motivation in following the learning process which can be seen from their enthusiasm in trying the counting beam either beads number aids (2) student activity increases which can be seen from their cohesiveness for solving the question and the cases which given in their group, (3) their mastery of concept ialso increases which is seen from the mean of their group mark from cycles 1 (35.8%), cycle 2 (40.6%) to cycle 3 (44.12%). The experiment was conducted in three cycles, with an overview of the following results; (1) student motivation for obeying the lessons seen rising from their passion to try and count beam props beaded numbers that exist in the learning process, (2) increased student activity is illustrated by their compactness to solve problems or cases are given in groups them, (3) also increased their mastery of the concept seen from the mean value of the group they began to cycle 1 (35.8%), cycle 2 (40.6%) up to 3 cycles (44.12%).

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.379
Threshold uncertainty score0.464

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
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 head0.147
GPT teacher head0.453
Teacher spread0.306 · 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

Citations21
Published2015
Admission routes1
Has abstractyes

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