4 research outputs found
PROSES BERPIKIR KOMPUTASIONAL SISWA DALAM MENYELESAIKAN SOAL PISA KONTEN CHANGE AND RELATIONSHIP DITINJAU DARI SELF EFFICACY
Get PDFPenelitian ini berfokus pada proses berpikir komputasional siswa dalam
menyelesaikan soal PISA konten change and relationship ditinjau dari self efficacy.
Subjek penelitian ini adalah siswa berumur 15 tahun. Metode penelitian ini
menggunakan pendekatan deskriptif kualitatif. Pengumpulan data menggunakan
angket, instrument tes dan wawancara. Analisis data pada penelitian ini meliputi,
pengumpulan data, reduksi data yang disajikan dalam bentuk teks dan penarikan
kesimpulan atau verifikasi.
Hasil penelitian dapat diketahui bahwa proses berpikir komputasional siswa
bahwa dalam menyelesaikan soal PISA konten change and relationship yang
mempunyai self efficacy tinggi dapat mencapai tahap dekomposisi, pengenalan
pola, abstraksi dan berpikir algoritma serta proses berpikir siswa dalam
melaksanakan rencana dapat menghubungkan masalah nyata menjadi masalah
matematis. Sedangkan siswa yang mempunyai self efficacy sedang mencapai tahap
dekomposisi, pengenalan pola, abstraksi dan berpikir algoritma dan dalam
melaksanakan rencana tidak menghubungkan masalah nyata ke masalah matematis
namun menggunakan logika. Sedangkan siswa yang mempunyai self efficacy
rendah hanya mencapai tahap dekomposisi dan pengenalan pola belum dilakukan
abstraksi dan berpikir algoritma karena proses berpikir siswa dalam melaksanakan
rencana menggunakan logika dan dalam menyelesaikan masalah tersebut tidak
memberikan kesimpulan jawaban serta langkah – langkah yang logis.
Kata kunci : berpikir komputasional, PISA, change and relationship, self efficac
Systematic Literature Review: Trends Computational Thinking and Mathematical Disposition in Mathematics Learning
Get PDFAbstract. This research aims to provide comprehensive information on the trend of computational thinking ability and mathematical disposition in mathematics learning. The method used was a Systematic Literature Review (SLR). Data was collected by reviewing articles on computational thinking skills and mathematical disposition published in 2019-2024. There were 15 articles, nine related to computational thinking ability and six related to mathematical disposition, obtained from Google Scholar and Scopus. The results of this study indicate that the methods and research designs that tend to be used for research on computational thinking ability and mathematical disposition tend to use quantitative research with quasi-experimental design, and PBL learning models are widely used. Research on thinking ability is predominantly carried out on elementary school students but for mathematical disposition at the junior and senior high school levels with geometry, algebra, and statistics mathematics materials. Computational thinking skills and mathematical disposition are two important aspects of mathematics education that are interrelated and influence each other.Keywords: computational thinking ability, mathematical disposition, mathematics learnin
How mathematical disposition shapes computational thinking in solving systems of linear equations: A flowchart-supported qualitative study
Get PDFComputational thinking (CT) is a vital 21st-century skill in mathematics education, enabling students to solve problems systematically through decomposition, pattern recognition, abstraction, and algorithmic thinking. However, students’ mathematical disposition—encompassing beliefs, habits of mind, and affective tendencies—may significantly influence CT development. Guided by the affective–cognitive interaction model, this study aimed to explore how mathematical disposition shapes students’ CT skills, particularly in solving systems of three-variable linear equations using self-constructed, flowchart-supported algorithmic representations. A descriptive qualitative approach was adopted, with six students (two each from high, medium, and low disposition levels, identified via questionnaire) participating. Data collection involved a disposition scale, CT test, interviews, and documentation. Findings revealed that high-disposition students successfully demonstrated all CT indicators and produced coherent flowcharts. Medium-disposition students showed variability: some met all criteria, while others faltered in algorithmic design. Low-disposition students managed only basic decomposition and pattern recognition, with incomplete abstraction and fragmented flowcharts. These results suggest a strong link between affective factors and cognitive performance in CT tasks. Implications highlight the importance of integrating disposition-aware scaffolding—such as interactive visual tools and guided reflection—to support diverse learners and enhance CT development in mathematics classrooms
Students' Computational Thinking Process in Solving PISA Problems of Change and Relationship Content Reviewed from Students’ Self Efficacy
Get PDFThis research focuses on the stages of students' computational thinking processes in solving PISA questions about change and relationship content in terms of self-efficacy. The subjects of this study were 15-year-old students of class X MIPA 3 MAN 1 Semarang City, totaling 22 students and selected 2 students who had high self-efficacy, 2 students who had moderate self-efficacy and 2 students who had low self-efficacy. This research method uses a qualitative descriptive approach. Data collection using questionnaires, test instruments and interviews. Data analysis in this study included data collection, data reduction presented in text form and drawing conclusions or verification. The results of the study show that students' computational thinking processes in solving PISA questions about change and relationship content that have high self-efficacy can reach the stages of decomposition, pattern recognition, abstraction and algorithmic thinking as well as students' thought processes in carrying out plans can link real problems into mathematical problems. Whereas students who have self-efficacy are reaching the stages of decomposition, pattern recognition, abstraction and algorithmic thinking and in carrying out plans do not connect real problems to mathematical problems but use logic. Whereas students who have low self-efficacy only reach the stages of decomposition and pattern recognition have not done abstraction and algorithmic thinking because students' thinking processes in carrying out plans use logic and in solving these problems do not provide conclusions of answers and logical step
