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ANALISIS KESULITAN SISWA DALAM MENYELESAIKAN SOAL TURUNAN FUNGSI ALJABAR DITINJAU DARI KECERDASAN EMOSIONAL

2022· article· en· W4382896878 on OpenAlexaff
Marselina Elizabeth Lepertery, Carolina Selfisina Ayal, Anderson Leonardo Palinussa

Bibliographic record

VenueJurnal Pendidikan Matematika Unpatti · 2022
Typearticle
Languageen
FieldMathematics
TopicMathematics Education and Pedagogy
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsEmotional intelligencePsychologyClass (philosophy)Test (biology)Mathematics educationMeaning (existential)SMA*Social psychologyMathematicsComputer scienceArtificial intelligenceCombinatorics

Abstract

fetched live from OpenAlex

This study aims to analyze and describe students difficulties in solving problems derived from algebraic functions in terms of emotional intelligence in class XI SMA Negeri 4 Central Maluku. This type of research is quantitative-qualitative research. The source of data in this study was students of class XI MIPA 3 SMA Negeri 4 Central Maluku with a total of 28 students, and the subjects in this study were 2 students including 1 student in the high emotional intelligence category, and 1 student representing the medium emotional intelligence category. The instruments used in this study were emotional intelligence questionnaires, algebraic function derivative test questions and interviews. The results showed that the difficulties experienced by students of class XI MIPA 3 SMA Negeri 4 Central Maluku in solving problems derived from algebraic functions include difficulties in understanding concepts, difficulties in understanding principles, and difficulties in operation. The results of the emotional intelligence questionnaire showed that the emotional intelligence of students of SMA Negeri 4 Central Maluku was in the high category. The results of the questionnaire averaged the percentage of emotional intelligence of students with high categories of 71.43%, emotional intelligence of students with moderate categories of 28.57%, and the average emotional intelligence of students with low categories of 0%, meaning that none of the students had low levels of emotional intelligence

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0340.000

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 head0.076
GPT teacher head0.359
Teacher spread0.283 · 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 designTheoretical or conceptual
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
Published2022
Admission routes1
Has abstractyes

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