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Record W2756721498 · doi:10.5539/ies.v10n10p39

The Application of Carousel Feedback and Round Table Cooperative Learning Models to Improve Student’s Higher Order Thinking Skills (HOTS) and Social Studies Learning Outcomes

2017· article· en· W2756721498 on OpenAlexvenueno aff
Harry Yusmanto, Budi Eko Soetjipto, Ery Tri Djatmika

Bibliographic record

VenueInternational Education Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationPsychologyHigher-order thinkingDocumentationMastery learningAction researchTest (biology)Cooperative learningData collectionTable (database)Social studiesPedagogyTeaching methodComputer science

Abstract

fetched live from OpenAlex

This Classroom Action Research aims to improve students’ HOTS (High Order Thinking Skills) and Social Studies learning outcomes through the application of Carousel Feedback and Round Table cooperative learning methods. This study was based on a model proposed by Elliott and was implemented for three cycles. The subjects were 30 female students of the eight graders of Junior High School Islam Terpadu Darul Azhar, South East Aceh, Aceh, Indonesia. The data collection instruments include observation, test, interview, and documentation. The data were obtained from pre and post-test of each cycle. The findings revealed that Carousel Feedback and Round Table cooperative learning models could increase students’ HOTS and Social Studies learning outcomes. The habit of giving time to the students to think, providing scaffolding, observing students’ activity, asking questions, discussing in groups, analyzing the results and providing feedback are the examples of activities that should be familiarized.

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)0.006
metaresearch head score (Gemma)0.015
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: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.442
Teacher spread0.368 · 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

Citations19
Published2017
Admission routes1
Has abstractyes

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