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All Journal Publikasi Pendidikan Jurnal Konseling dan Pendidikan Al-Jabar : Jurnal Pendidikan Matematika JPPPF (Jurnal Penelitian dan Pengembangan Pendidikan Fisika) JPM : Jurnal Pendidikan Matematika Jurnal Riset Pembelajaran Matematika Sekolah Kognitif: Jurnal Riset HOTS Pendidikan Matematika JRFES Al-Irsyad Journal of Mathematics Education International Journal of Theory and Application in Elementary and Secondary School Education (IJTAESE) Jurnal Pendidikan Progresif
Gusti, Valeria Yekti Kwasaning
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Humanities Computer Science & IT Education Library & Information Science Mathematics Physics Social Sciences Other

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Bridging Pedagogy and AI: A Systematic Review of Deep Learning in Mathematics Education Gusti, Valeria Yekti Kwasaning; Amastini, Fitria
Jurnal Pendidikan Matematika (JPM) Vol 11 No 2 (2025): Jurnal Pendidikan Matematika (JPM)
Publisher : Department of Mathematics Education, Faculty of Teacher Training and Education, Universitas Islam Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33474/jpm.v11i2.24319

Abstract

Deep learning is interpreted differently in educational and computational contexts. The purpose of this study is to systematically examine how deep learning is conceptualized and applied in mathematics education research, and to evaluate the extent to which these applications align with the Indonesian Ministry of Education and Culture’s (Kemendikbud) definition of deep learning. A systematic literature review (SLR) was conducted using PRISMA guidelines. Searches in ScienceDirect, Scopus, Springer Link, and ProQuest produced 1,881 records containing the term “deep learning,’” published between 2015 and 2025. After duplicate removal and screening, 16 peer-reviewed journal articles explicitly addressing deep learning in mathematics education were included. Two main interpretations were found. Eleven studies framed deep learning pedagogically, focusing on conceptual understanding, problem-solving, collaborative learning, and real-world application. Five studies adopted a computational framing, using deep neural networks and other machine learning techniques for prediction, error analysis, adaptive instruction, and automated feedback. While the majority aligned with Kemendikbud’s pedagogical emphasis, some studies treated deep learning purely as a technical method, without explicit links to student-centered outcomes. The review highlights a conceptual gap between pedagogical and computational uses of deep learning in mathematics education. Bridging this gap requires interdisciplinary collaboration between educators and technology developers to ensure technological applications support meaningful learning. The findings provide a reference for aligning global research on deep learning with national education policy, ensuring relevance for curriculum design and classroom practice. 
Unlocking Students’ Success: Math Learning via Instagram Filter Innovations Gusti, Valeria Yekti Kwasaning; Risnawati, Erna; Tanjung, Khoriyah Shofiyah
Jurnal Pendidikan Progresif Vol 14, No 3 (2024): Jurnal Pendidikan Progresif
Publisher : FKIP Universitas Lampung

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Abstract

Unlocking Students’ Success: Math Learning via Instagram Filter Innovations Objectives: This study investigates the impact of Instagram-based filter learning media on junior high school students' learning outcomes in mathematics, focusing on relations and functions. Methods: Using a quantitative methodology, the study employed a one-group pretest-post-test design to measure learning outcomes before and after interventions. Data were analyzed using paired t-tests and Cohen’s d to assess both statistical and practical significance. Findings: The findings reveal that Instagram filters significantly improved students' mathematical understanding, with average scores increasing from 61,13 (pre-test) to 71,63 (post-test). The improvement was statistically significant (p < 0,05), with a large effect size (Cohen’s d = 1,02), indicating a substantial impact of the intervention. Furthermore, the use of Instagram filters enhanced engagement and motivation, helping students grasp abstract concepts more effectively. In terms of specific indicators, for Conceptual Understanding, the pretest score was 26,00, and the post-test score increased to 30,38. The N-Gain for this indicator was 31,25%, indicating a moderate improvement in students’ understanding of key concepts. For Procedural Knowledge, the pretest score was 24,00, and the post-test score increased to 27,75. The N-Gain for this indicator was 20,31%, showing low improvement in students’ ability to apply mathematical procedures. For Problem-Solving Skills, the pretest score was 10,25, and the post-test score increased to 13,50. The N-Gain for this indicator was 33.33%, reflecting moderate improvement in students’ ability to apply mathematical knowledge to solve real-world problems involving relations and functions. Conclusions: The results highlight Instagram's potential as an effective educational tool for mathematics. Instagram filters engage students while aligning with Indonesia's Merdeka Curriculum, promoting innovative, student-centered learning. This research offers valuable insights for educators looking to integrate interactive technologies into modern teaching methods. Keywords: instagram filter, mathematics learning media, relations and functions.DOI: http://dx.doi.org/10.23960/jpp.v14.i3.2024128
Improving Indonesian students' mathematical literacy with brain-based learning: a comparative study of pisa scores Kandaga, Thesa; Krisnadi, Elang; Nurhayati, Suci; Gusti, Valeria Yekti Kwasaning
Jurnal Konseling dan Pendidikan Vol. 12 No. 3 (2024): JKP
Publisher : Indonesian Institute for Counseling, Education and Therapy (IICET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29210/1118500

Abstract

This research is driven by Indonesia's comparatively low performance in the 2023 PISA assessment, which indicates a significant deficiency in students' capacity to solve literacy-based mathematical problems, largely attributable to the absence of comprehensive, structured guidance. This consequently gives rise to low levels of mathematical literacy, thereby underscoring the necessity for enhanced learning methodologies. The objective of this study is to evaluate the efficacy of the Brain-Based Learning (BBL) model in enhancing students' mathematical literacy abilities in comparison to the traditional learning approach. This study employed a quasi-experimental methodology with an unequal control group design. The research subjects were 72 grade VIII students at SMPN 1 Baleendah, comprising 36 experimental group students (grade VIII K) and 36 control group students (grade VIII L). The data were obtained through mathematical literacy tests and subsequently analysed using descriptive and inferential statistics with the assistance of IBM SPSS 24.0 software. The results of the n-gain analysis indicated that the data were normally distributed with homogeneous variances. The results of the independent sample t-test indicated a statistically significant difference in the improvement of mathematical literacy skills between the two groups, with the BBL group exhibiting a greater degree of improvement than the conventional method. These findings contribute to the scientific literature on the effectiveness of the Brain-Based Learning (BBL) model in addressing low mathematical literacy in Indonesia, with statistically significant improvements. This research provides new insights into the application of brain-based learning models in the Indonesian educational context.
Co-Authors Adzima, Khaola Rachma Amastini, Fitria Aprianti, Rika Aryaty, Unny Ayu Fahimah Diniyah Wathi Herliana, Fitria Krisnadi, Elang Mohamad, Liana Nadiyyah, Khoirotun Nilam Novita Sari Rahmanadia, Hyunisa Rahmawati, Orizka Desmi Risnawati, Erna Siti Utami Dewi Ningrum Suci Nurhayati Suci Nurhayati Tanjung, Khoriyah Shofiyah Thesa Kandaga Tiara Husnul Khotimah Uliya Khoirun Nisa Wulandari, Nisa A’rafiyah Tri