The Effectiveness of Deep Learning-Based PjBL on Students' Reasoning Ability and Motivation in the Science Curriculum Analysis Course

This study aims to identify the effectiveness of learning using Deep Learning-based PjBL on students' reasoning ability and motivation, specifically in the curriculum analysis course material. This course studies the concepts, components, and documentation of the curriculum, aiming for students, especially those in the Science Education study program at HKBP Nommensen University Medan, to be able to analyze the relationship between learning objectives, learning outcomes, materials, learning processes, concepts, assessment components, and student characteristics. The method used in this quantitative research is a quasi-experimental method with a posttest-only control group design approach. Hypothesis testing used the t-test and MANOVA test to determine the effectiveness of Deep Learning-based PjBL on students' reasoning ability and motivation in the Science curriculum analysis material. The results obtained in this study showed a significant result of 0.003 < 0.005 with a t-count value of 3.102 > t-table 2.005, indicating that students who received Deep Learning-based PjBL learning had better reasoning abilities compared to students who received conventional learning. Similarly, for a significant value of 0.047 < 0.05 with a t-count value = 2.030 > t-table = 2.005, it can be concluded that the effect of Deep Learning-based PjBL on learning motivation is in the medium category