Deka Anjariyah, Feriyanto, Rizky Oktaviana Eko Putri

DEEP LEARNING-BASED JIGSAW: BOOSTING STUDENT ENGAGEMENT AND PERFORMANCE IN STATISTISC LEARNING

Introduction

Deep learning-based jigsaw: boosting student engagement and performance in statistisc learning . Boost junior high student engagement & statistics learning with a deep learning-based Jigsaw model. This study shows improved activeness and performance in statistics.

Abstract

Learning statistics remains a persistent challenge for many junior high school students due to abstract concepts and limited engagement in traditional classrooms. Therefore, improving students’ understanding and motivation in statistics learning requires innovative learning models that promote active, meaningful participation. This study investigates the impact of a novel deep learning-based Jigsaw model on Grade VIII students’ engagement and statistics learning outcomes at SMPN 2 Mojosari. Integrating principles of mindful, meaningful, and joyful learning, the model emphasizes understanding, application, and reflection to foster deeper cognitive engagement. Using a quantitative one-shot case study design with 32 students, data were gathered through student activeness questionnaires and statistics achievement tests. Findings reveal that this approach significantly enhances student activeness, promoting collaboration and critical thinking during discussions, presentations, and reflections. Most students demonstrated medium to high engagement levels, while average learning outcomes surpassed the school’s minimum mastery criteria. Given the urgent need to improve statistical literacy and student motivation in junior high schools, the deep learning-based Jigsaw model offers a promising, effective alternative strategy to traditional teaching methods. This research contributes valuable evidence supporting innovative, student-centered learning in mathematics education.

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