MétaCan
Menu
Back to cohort
Record W2241627967 · doi:10.29303/jpm.v4i1.181

IMPLEMENTASI TEKNIK JIGSAW DALAM PEMBELAJARAN GEOMETRI SEBAGAI UPAYAMENINGKATKAN HASILBELAJAR DAN KEMANDIRIAN BELAJAR SISWA KELAS IX SMU NEGERI 1 DEPOKYOGYAKARTA

2009· article· id· W2241627967 on OpenAlexaff
Endah Retnowati, Jailani Jailani

Bibliographic record

VenueJURNAL PIJAR MIPA · 2009
Typearticle
Languageid
FieldSocial Sciences
TopicSTEM Education
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsJigsawHumanitiesMathematics educationPsychologyArt

Abstract

fetched live from OpenAlex

Abstrak. Penelitian tindakan kelas ini bertujuan untuk meningkatkan hasil belajar dan kemandirian belajar siswa melalui teknik pembelajaran jigsaw. Subjek penelitian ini adalah 36 siswa kelas XI Jurusan IPA SMU Negeri1 Depok, Yogyakarta. Langkah-langkah penelitian tindakan kelas mengacu pada model Kemmis dan McTaggart dimana setiap siklus tindakan meliputi perencanaan, tindakan, observasi, dan refleksi. Instrumen penelitian terdiridari lembar pengamatan pelaksanaan pembelajaran dan pengamatan terhadap partisipasi siswa, kuis, angket kemandirian belajar, angket sikap siswa dan wawancara. Penelitian ini terlaksana dalam 2 siklus. Hasil penelitianmenunjukkan bahwa pembelajaran geometri dengan menerapkan teknik jigsaw dapat meningkatkan hasil belajar siswa, yaitu sebanyak 78.13% siswa tuntas belajar pada siklus 2 dengan adanya tindakan antara lain visualisasimateri dengan software CABRI, pemberian bimbingan atau petunjuk dalam mengaktifkan proses kognitif siswa untuk memahami materi, memvisualisasikan konsep melalui gambar yang menarik menggunakan presentasi dengan software CABRI dan melibatkan siswa dalam penilaian kuis. Sebelum siklus 1, sebanyak 32.26% siswa mampunyai kemandirian belajar kualifikasi atas dan setelah siklus 2 meningkat menjadi 37.93% siswa. Peningkatan kemandirian belajar terlihat menonjol terutama dalam hal menumbuhkan motivasi belajar, merumuskan tujuan belajar dan mengevaluasi hasil belajarnya.Kata Kunci: Jigsaw, hasil belajar matematika, kemandirian belajarAbstract. The classroom action research has been done to improve performance and self-regulated learning through a jigsaw learning technique. The subject was 36 grade 11 students majoring in Natural Science; at a publichigh school namely SMU Negeri 1 Depok, Yogyakarta, Indonesia. The classroom action research followed the model introduced by Kemmis and McTaggart, in which a cycle consist four steps: planning, action, observationand reflection. The instruments to collect the data were observation sheets of learning activity and students’ participation during learning, quizzes, questionnaire of self-regulated learning and attitude, as well as interviewsheet. There were two cycles of learning in the research. The result indicated that the geometry lesson implementing the jigsaw technique 78.13% students master the learning competence after the second cycle. Specifically, the actions were visualization to be learnt material using CABRI application, giving guidance or hint to activate students’ cognitive process while understanding material, using interactive pictures when presenting aconcept and involved students when marking the quizzes’ results. The percentage of students who had selfregulated learning on high level in the first and second cycles were 32.26% and 37.93% respectively. The improvement of self-regulated learning was mostly in self learning motivation, defining learning goals and selflearning evaluation.Keywords: jigsaw, mathematics learning performance, self-regulated learning

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0050.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0300.011

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 head0.028
GPT teacher head0.331
Teacher spread0.304 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueJURNAL PIJAR MIPASame topicSTEM EducationFrench-language works237,207