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Record W4387217175 · doi:10.59697/jik.v5i1.319

PENGEMBANGAN MEDIA PEMBELAJARAN BERBASIS VIDEO SPARKOL DENGAN MENGGUNAKAN MEDIA INTERNET DALAM PEMBELAJARAN MATEMATIKA SMP NEGERI 1 DAN 2 KECAMATAN TANAH JAWA

2021· article· en· W4387217175 on OpenAlex

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 Informatika Kaputama (JIK) · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicSTEM Education
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsThe InternetCurriculumComputer scienceMathematics educationCoding (social sciences)Identification (biology)MultimediaWorld Wide WebPedagogyPsychologyMathematics

Abstract

fetched live from OpenAlex

The existence of the internet and all existing facilities can provide new knowledge or browse teaching materials for teachers so that the internet is an easier learning medium and can enrich teachers' insights. This study aims to determine the use of sparkol video as a learning resource for mathematics teachers of SMP Negeri Tanah Jawa District. Sparkol video-based internet learning media is made with the Videoscribe application, as a learning medium that can be used in schools and outside of school. The research aims to develop learning media that can be accessed freely on the Internet. Sparkol video-based internet learning model used is videoscribe which is open and mass. The learning curriculum also provides a set of learning tools covering, various questions, quizzes, and forms of assignments. The research implementation steps follow the general pattern of scientific research with an object-oriented approach with the stages of identification (identification), analysis (analysis), design (design), coding (programming) and testing (testing). This study produced an outcome that was targeted at the results of this study in the form of articles to be published in an Accredited National Journal. This article also aims to disseminate the results of research and science that can be used to broaden the knowledge and development of science in the future, especially in finding association rules between indicators of achieving competency standards tested in mathematics.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.437
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.004
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.026
GPT teacher head0.290
Teacher spread0.264 · 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