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Record W3112910631 · doi:10.20961/shes.v3i3.46659

Application of The Paikem Approach to Improve Vocabulary Material Learning Outcomes in SD Negeri 2 Karangwuni

2020· article· en· W3112910631 on OpenAlexaff
Eko Nuryanto

Bibliographic record

VenueSocial Humanities and Educational Studies (SHEs) Conference Series · 2020
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsIndonesianVocabularyMathematics educationAction researchPsychologyMeaning (existential)PedagogyLinguistics

Abstract

fetched live from OpenAlex

<p><em>This study aims to increase understanding of vocabulary and their meanings by applying modeling techniques to grade II elementary school students. This study was designed in two cycles, with the research subjects of grade II students of SD N 2 Karangwuni in Pringsurat District, Temangggung Regency with a total number of students. 9 students. The research design used was the Classroom Action Research (PTK) spiral model from Kemmis and Taggart which included four stages of research, namely planning, implementing, observing, and reflecting. Students' understanding of the meaning of vocabulary has increased each cycle. Increased understanding of the meaning of students' vocabulary can be seen from the average cycle I only 66.6%. While in cycle II the average score increased by 88.8%. It was concluded that using the Paikem Approach which was carried out in accordance with the learning steps included the application of Active, Innovative, Creative, Effective, and Fun Learning, making learning conclusions, providing evaluation and closing the learning process in Indonesian subjects can improve student learning outcomes in grade 2 SD N 2 Karangwuni, Pringsurat District, Temanggung Regency Based on this research, teachers should be able to choose a learning model that is in accordance with the character of students, so that students are motivated to learn, so that students are able to understand subject matter and interesting. </em></p>

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.728
Threshold uncertainty score0.520

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.074
GPT teacher head0.307
Teacher spread0.233 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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