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Record W2946216013 · doi:10.24269/dpp.v6i3.1376

USE OF MODULE IN LEARNING GUIDELINESTO IMPROVE STUDENT LEARNING OUTCOMES

2019· article· en· W2946216013 on OpenAlex
Ari Metalin Ika Puspita, Agus Santosa

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJurnal Dimensi Pendidikan dan Pembelajaran · 2019
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsClass (philosophy)Action researchMathematics educationComputer scienceQualitative propertyReflection (computer programming)PsychologyArtificial intelligenceMachine learning

Abstract

fetched live from OpenAlex

This study aims to determine the improvement of students' learning outcomes in the third grade of elementary school in the use of modules on tutoring in SDN IV Tanggung in Tulungagung District. The study design uses a classroom action research consisting of two cycles. Each silklus consists of 4 stages, namely the stage of planning, implementation, observation, and reflection. The subjects of this study are class III students who numbered 42 students in SDN IV Tanggung. Data on the use of Module taken from the observation and interview. The data have been analyzed using qualitative descriptive analysis. The results of this study using module ni indicate that can improve student learning outcomes. Before using student learning result module shows percentage 50% in cycles I and At Cycle II reach 71,69%%. Based on the observations during the implementation of the action on the cycle I and cycle II found the results showed an increase in some aspects specified in this study.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.035
GPT teacher head0.333
Teacher spread0.298 · 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