TY - GEN
T1 - Enhancing Learning Through a Web-Based OER
T2 - 8th International Conference on Technology in Education, ICTE 2025
AU - Song, Lindsay Elle Chiara
AU - Lee, Anson Hwong
AU - Towey, Dave
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
PY - 2025/11/26
Y1 - 2025/11/26
N2 - Software testing is essential for ensuring system reliability. However, some systems face the Oracle Problem, where the correctness of outputs cannot easily be determined. In cybersecurity, password-based authentication is widely used, and tools such as John the Ripper (JtR) can expose vulnerabilities. This paper applies Metamorphic Testing (MT) and Metamorphic Exploration (ME) to JtR, identifying flaws and enhancing system understanding. A web-based Open Educational Resource (OER) was also developed with interactive features to promote the use of MT and support learner engagement. The OER includes quizzes for self-assessment and a feedback mechanism explaining incorrect answers. An initial attempt was made to use Large Language Models (LLMs) to generate on-demand quiz questions and feedback. However, survey participants noted that AI-generated questions were too simple and often contained context clues. This paper will be of interest to educators and researchers in software testing, cybersecurity, and AI-assisted education.
AB - Software testing is essential for ensuring system reliability. However, some systems face the Oracle Problem, where the correctness of outputs cannot easily be determined. In cybersecurity, password-based authentication is widely used, and tools such as John the Ripper (JtR) can expose vulnerabilities. This paper applies Metamorphic Testing (MT) and Metamorphic Exploration (ME) to JtR, identifying flaws and enhancing system understanding. A web-based Open Educational Resource (OER) was also developed with interactive features to promote the use of MT and support learner engagement. The OER includes quizzes for self-assessment and a feedback mechanism explaining incorrect answers. An initial attempt was made to use Large Language Models (LLMs) to generate on-demand quiz questions and feedback. However, survey participants noted that AI-generated questions were too simple and often contained context clues. This paper will be of interest to educators and researchers in software testing, cybersecurity, and AI-assisted education.
KW - Large Language Model (LLM)
KW - Metamorphic Exploration (ME)
KW - Metamorphic Testing (MT)
KW - Open Educational Resource (OER)
KW - Password Cracker
UR - https://www.scopus.com/pages/publications/105023590611
U2 - 10.1007/978-981-95-4499-8_3
DO - 10.1007/978-981-95-4499-8_3
M3 - Conference contribution
AN - SCOPUS:105023590611
SN - 9789819544981
T3 - Communications in Computer and Information Science
SP - 25
EP - 35
BT - Technology in Education. Smart and Innovative Learning - International Conference on Technology in Education, ICTE 2025, Proceedings
A2 - Cheung, Simon K. S.
A2 - Liu, Xiaojun
A2 - Xu, Guoai
A2 - Kwok, Lam-For
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 10 December 2025 through 12 December 2025
ER -