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Record W3132674906 · doi:10.20961/shes.v4i1.48564

Improving Student Asking Activity by Using Learning Start with a Question (LSQ) Learning Method

2021· article· en· W3132674906 on OpenAlexaff
Patimah Delasari, Fajar Nugraha, Riza Fatimah Zahrah

Bibliographic record

VenueSocial Humanities and Educational Studies (SHEs) Conference Series · 2021
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsMathematics educationAction researchPsychologyData collectionTest (biology)MathematicsStatistics

Abstract

fetched live from OpenAlex

<p><em>This study aims to meet whether the Learning Start With A Question (LSQ) learning method can be used to improve the results of questioning activeness in grade IV SD Negeri Pereng. This research method is Classroom Action Research (PTK), using the model of Kesmis and Mc. Taggart. The subjects in this study were the fourth grade students of SD Negeri Pereng, Tasikmalaya Regency, totaling 17 students. Data collection techniques in the form of student observation sheets, tests to determine the ability to ask questions and administrative learning documents. The results showed that the use of the Learning Start With A Question (LSQ) learning method in social studies learning could increase the activeness of the fourth grade students of SD Negeri Pereng, Tasikmalaya Regency. Based on the test results obtained by students at the end of each cycle, it showed an increase in student questioning activity. The average completeness in the first cycle was 47.35, and the average completeness in the second cycle was 67.64, while the average of the third cycle was 78.82. The results show that the quality of the learning process, especially in questioning activeness, has increased.</em><em></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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.561
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.001
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 head0.094
GPT teacher head0.381
Teacher spread0.287 · 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.

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

Citations0
Published2021
Admission routes1
Has abstractyes

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