Conference paper 2023

Generating Automated Assistance Mechanism in Android Programming Self-learning System Using Automatic Testing Tools

2023 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2023
Conference · pp. 445-450
Abstract

Nowadays, computer-assisted learning (CAL) has experienced a significant increase in demand for self-learning purposes with the mechanism of automated assistance to facilitate assessments and provide instant feedback to learners. Using automatic testing, test cases can be executed repeatedly, providing informative feedback to assist students in correcting the written code referring to the assignment specifications. This research experiences an automated testing mechanism in an Android programming self-learning system to serve several important functions for learning experience enhancement. By adopting the TDD method and utilizing automatic software testing tools, the testing process combines unit testing with JUnit and integration testing with Robolectric. It ensures the quality and functionality of the application under test based on learning assignment's specifications. Utilizing the assertion methods realizes the code verification referring to expected behavior and potentially produces informative feedback that can be captured to provide automated assistance. Evaluation through the test code implementation in the Basic Application learning topic shows that automatic testing tools cover the automated assistance feature in the validations of project configuration and UI components. © 2023 IEEE.

Keywords

Author Keywords

Android automated assistance automatic testing computer-assisted learning self-learning

Index Keywords

E-learning Learning systems Computer aided instruction Integration testing Automation Specifications Self-learning Test case Testing tools Android (operating system) Automatic testing Android Assistance mechanisms Automated assistance Automated testing Computer assisted learning Research experience Self learning system
Author Affiliations
Department of Information Technology, State Polytechnic of Malang, Malang, Indonesia
Department of Computer and Informatics Engineering, Politeknik Negeri Jakarta, Jakarta, Indonesia
Department of Computer Engineering, Universitas Negeri Makassar, Makassar, Indonesia
Department of Electronics and Communication Engineering, Okayama University, Okayama, Okayama, Japan
Department of Electrical Engineering, State Polytechnic of Malang, Malang, Indonesia
Funding & Acknowledgements
Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi, MECRT
The Directorate General of Vocational Education, which is part of the Ministry of Education, Culture, Research, and Technology, provided funding for this study. Three universities, State Polytechnic of Malang, Okayama University Japan, and State University of Makassar, have collaborated to develop this research as part of the Vocational Products Research scheme in 2023. The authors extend their sincere gratitude to all academics, educators, collaborators on research, and
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