Msy Elga Fitri Aisyah
Bina Sriwijaya Institute of Technology and Business, Indonesia

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

Development of a Web-Based Programming Practice System with Automated Feedback for Beginner Students Msy Elga Fitri Aisyah; Indah Rahma Sari
Journal Innovation in Information and Computer Technology Vol. 2 No. 2 (2025): (May) Journal Innovation in Information and Computer Technology (JICTECH)
Publisher : PT. Altaf Publishing Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70895/jictech.v2i2.128

Abstract

This study addresses the challenges faced by beginner students in learning programming, particularly the lack of continuous practice and timely feedback. Programming requires an iterative learning process, yet many existing learning environments still rely on delayed and manual evaluation, which limits students’ ability to improve effectively. To overcome this issue, this research aims to develop a web-based programming practice system equipped with automated feedback.The system was developed using a structured approach and integrates an interactive coding environment, automated evaluation mechanisms, and real-time feedback features. It allows students to write, execute, and submit code directly through the platform while receiving immediate responses based on predefined test cases and code analysis.The results show that the proposed system functions effectively in supporting programming practice for beginner learners. The system demonstrates strong performance in terms of usability, response time, and feedback accuracy. In addition, the availability of instant feedback encourages students to engage in repeated practice, which contributes to improved problem-solving skills and learning motivation.This study concludes that integrating web-based learning with automated feedback provides a practical and scalable solution for programming education. The system not only enhances learning efficiency but also reduces the dependency on manual assessment. Future development may focus on improving feedback depth and personalization to better support conceptual understanding.