Development of a Genially-Based Teaching Module Based on the Deep Learning Approach for Basic Vocational Subjects in the Mechatronics Engineering Program at SMKN 1 Cilegon

Abstract

Learning activities in the Basic Vocational Program subject at SMKN 1
Cilegon are still predominantly conducted through conventional instructional
methods, accounting for 84.4% of classroom practices and limiting student’s active
engagement. This study developed an interactive learning module using the
Genially platform integrated with a deep learning approach for the topic of Active
and Passive Electronic Components and evaluated its feasibility, practicality, and
effectiveness through a Research and Development (R&D) method based on the
ADDIE model, consisting of analysis, design, development, implementation, and
evaluation stages. The participants were 34 tenth-grade students of Mechatronics
Engineering at SMKN 1 Cilegon, with data collected through expert validation
instruments, student response questionnaires, and pretest–posttest assessments.
The results showed that the module was highly feasible, with validation scores of 71
from media experts (X ≥ 54) and 79 from material experts (X ≥ 60), while student
responses indicated very high practicality with an average score of 84.5 (X ≥ 72). In
terms of effectiveness, learning outcomes improved significantly, as shown by an
increase in the average score from 51.6 on the pretest to 91.2 on the posttest, with
an N-Gain value of 0.822 categorized as high, confirming that the developed module
is feasible, practical, and effective in enhancing student learning outcomes and
supporting student-centered learning in vocational education.