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Mathematical Resilience and Academic Science Emotion in Students: New Insights from Canonical Correlation Analysis Umi Mahmudah; Moh. Muslih
Jurnal Penelitian Pendidikan IPA Vol 11 No 4 (2025): April
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i4.8838

Abstract

This research aims to explore the correlation between mathematical resilience and academic science emotion and provides new insights into this association through the application of canonical correlation analysis. The study involved a sample of 191 students from a state university in Central Java, Indonesia. Participants completed self-report measures to assess their levels of mathematical resilience and academic science emotion. Data was collected by randomly distributing questionnaires to students, evaluating five indicators of academic resilience (self-efficacy, planning, control, low anxiety, persistence) and three indicators of science emotions (class-related emotions, learning-related emotions, test emotions). Canonical correlation analysis was conducted to examine the multidimensional relationship between these variables and identify underlying patterns and associations. The analysis, conducted using R programming, yielded canonical correlation coefficients of 0.785, 0.396, and 0.119, indicating a significant positive linear relationship between the analyzed variables. These findings provide insight for educators and policymakers to design interventions that enhance both mathematical resilience and emotional well-being in science education
Bibliometric analysis of deep learning research trends as a pedagogical approach to science learning in elementary schools (2021–2025) Mega Setya Handayani; Mawaddah Warohmah; Umi Mahmudah
Jurnal Penelitian Ilmu Pendidikan Vol. 18 No. 2 (2025): July–December
Publisher : Fakultas Ilmu Pendidikan, Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jpip.v18i2.94621

Abstract

The dynamics of education in the 21st century demand a pedagogical transformation towards holistic competency development. This study aims to analyze the research trend of Deep Learning as a pedagogical approach to social studies learning in elementary schools. Using bibliometric methods, this study mapped the intellectual structure of 574 scientific articles (2021–2025) sourced from Google Scholar through the Publish or Perish software and VOSviewer. The results of the network visualization analysis identified five main clusters that showed a shift in focus from the technical aspects of computing towards the integration of intelligent technologies in classroom practice. The publication trend jumped significantly after 2023, with the latest topics focusing on critical thinking, creativity, and artificial intelligence (AI) literacy. Density analysis reveals research gaps: the literature is dominated by science and STEM content, with little exploration of the social dimension (social studies) or local wisdom. This research concludes that Deep Learning has developed into a technological entity that supports the achievement of the 8 Dimensions of the Graduate Profile through the principles of mindful, meaningful, and joyful learning. The practical implications provide guidance for educators to integrate technology scaffolding with in-depth inquiry, shaping adaptive lifelong learners in the era of disruption.