Model Jigsaw Menggunakan Pendekatan Deep Learning Terhadap Kepercayaan Diri Siswa Sekolah Dasar

Zifora Putri Keyla, Idam Ragil Widianto Atmojo, Matsuri Matsuri Matsuri

Abstract

Self-confidence is a crucial aspect supporting student engagement during learning. However, the learning process is still dominated by teachers, resulting in students being less confident in expressing their opinions and participating actively. The purpose of this study was to determine the effectiveness of the Jigsaw model using a Deep Learning approach on student self-confidence in fifth-grade science learning in elementary school. The researchers applied a quantitative approach with a quasi-experimental method using a Nonequivalent Control Group Design. The study sample consisted of 59 students, with 33 students in the experimental class and 26 students in the control class. Data were collected through a self-confidence test in the form of a Situational Judgment Test (SJT), observation sheets, and documentation. Data were then analyzed using an Independent Samples t-test using IBM SPSS Statistics 25. The experimental class achieved a higher average posttest score of 93.64 compared to the control class of 86.00. Based on the results of the hypothesis test, this difference was declared significant with a t-value of 8.151 with a significance level of 0.000 (<0.05). Thus, the Jigsaw model using a deep learning approach has an impact on student self-confidence. The implications of this discussion clarify that the Jigsaw model using a deep learning approach is an effective learning alternative for developing student self-confidence in science learning.

Keywords

Jigsaw Model; Deep Learning; Self Confidence; Science; Elementary School

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DOI: https://doi.org/10.20961/ddi.v14i6.125313

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