Deep Learning Based On Joyful Learning In Increasing Learning Motivation

Abstract

This study examines the application of the Joyful Learning-based Deep Learning  model to students' learning motivation. In the context of 21st century learning challenges, increasing students' intrinsic motivation is an important aspect to achieve meaningful and sustainable learning outcomes. This study aims to find out the extent to which the application  of the Deep Learning approach  combined with the principles  of Joyful Learning can significantly increase students' learning motivation. This study used a quantitative approach with a quasi-experimental design, involving two groups of students: the experimental group that was treated with  a Joyful Learning-based Deep Learning  learning model, and the control group that used conventional learning methods. Data collection was carried out through a validated learning motivation questionnaire, with pre-test and post-test formats. The sample consisted of 60 students of An Nur Al-Muntahy Junior High School who were selected using purposive sampling techniques. Data analysis used descriptive statistics and paired t-tests to see differences in motivation scores before and after treatment. The results showed a significant increase in the learning motivation of the experimental group, with an average post-test score of 85 compared to the pre-test of 65. In contrast, the control group only showed an increase in scores from 64 to 70. These findings show that the integration of emotional elements, meaningful context, and a pleasant learning atmosphere in the learning process is able to increase students' cognitive engagement in a profound way. In conclusion, this research contributes to the development of a student-centered learning approach by combining depth of thinking and emotional well-being. Further research is suggested to test the long-term impact and application of this model at various levels of education.