Journal of Deep Learning, Computer Vision and Digital Image Processing
Volume 4 Issue 3 September 2026

Development of OOPify as a Visual Block Programming Environment for OOP Learning and Group-Level Practical Performance

Naufal Oktavian (Universitas Pendidikan Indonesia)
Yogi Prasetyo (Universitas Pendidikan Indonesia)
Muhammad Rafi Valliansyah (Universitas Pendidikan Indonesia)
Lala Septem Riza (Universitas Pendidikan Indonesia)



Article Info

Publish Date
10 Sep 2026

Abstract

Purpose – Object-Oriented Programming (OOP) is challenging for students because its abstract concepts are difficult to translate into practical programming solutions. This study developed OOPify, a visual block-based learning environment for OOP practical activities, and examined changes in students’ conceptual understanding and group-level practical performance.Methods – The study employed a Research and Development approach using the ADDIE model and a one-group pretest–posttest design. Participants were 34 vocational high school students in West Java, Indonesia. Data were collected through OOP conceptual understanding tests, group-level practical performance observations, expert validation, and student response questionnaires.Findings – OOPify was rated feasible (82.5%) by one expert. The Wilcoxon Signed-Rank Test indicated a significant improvement in OOP conceptual understanding (p < .001), with 70.59% of students achieving moderate-to-high N-Gain and an average N-Gain of 0.50. Group-level practical performance, covering psychomotor, cognitive, and collaborative indicators, averaged 85.22% across Meetings 2 and 3. However, these results represent preliminary observational evidence because formal inter-rater reliability was not assessed.Research implications – Visual block-based learning environments may support OOP learning by reducing syntactic complexity and allowing students to focus on concepts and problem-solving. Generalizability is limited by the single-school context and absence of a control group.Originality – This study integrates visual block programming, OOP practical activities, and group-level practical performance within a single learning environment.

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Journal Info

Abbrev

DECODING

Publisher

Subject

Computer Science & IT

Description

The Journal of Deep Learning, Computer Vision and Digital Image Processing (DECODING), covers all topics of artificial intelligence and soft computing and their applications, including but not limited to: • Neural networks • Reasoning and evolution • Intelligent search • Intelligent planning ...