An Interactive Mobile Learning Platform for Digital Forensics Education: Design, Development, and Pedagogical Evaluation

Krommavut Nongnuch; Saowalak Leelawongsarote; Tawan Khunarsa; Suthin Kasetratanachai

doi:10.3991/ijim.v20i20.62646

Authors

Krommavut Nongnuch Rajamangala University of Technology Suvarnabhumi, Ayutthaya, Thailand https://orcid.org/0000-0002-3492-9745
Saowalak Leelawongsarote Rajamangala University of Technology Suvarnabhumi, Ayutthaya, Thailand https://orcid.org/0009-0004-8171-8790
Tawan Khunarsa Rajamangala University of Technology Suvarnabhumi, Ayutthaya, Thailand
Suthin Kasetratanachai Rajamangala University of Technology Suvarnabhumi, Ayutthaya, Thailand https://orcid.org/0009-0003-8036-5148

DOI:

https://doi.org/10.3991/ijim.v20i20.62646

Keywords:

Mobile learning, digital forensics education, interactive learning platform, cybersecurity education, gamification, higher education

Abstract

Demand for digital forensics (DF) graduates is rising, yet undergraduate DF teaching still depends on costly desktop tools, fixed laboratory slots, and limited time for repeated practice— barriers that weigh most heavily on resource-constrained universities. This paper presents DFLearn-Mobile, a cross-platform mobile application for undergraduate DF education built with Flutter, Node.js, and MongoDB. The app delivers six short modules aligned with the NIST DF process model, each closing with an interactive task and quiz, plus a capstone branching case for end-to-end procedural reasoning. We evaluated the platform with N = 80 undergraduates at Rajamangala University of Technology Suvarnabhumi using a quasi-experimental design combining pre- and post-tests, the system usability scale (SUS), an adapted technology acceptance model (TAM) instrument, and server-side analytics. DF knowledge improved significantly (Wilcoxon V = 3,240, p < .001; all 80 learners improved; mean normalized gain <g> = 0.586, medium band). The SUS score (M = 61.03) fell significantly below the 68 benchmark, identifying usability priorities for the next iteration. TAM construct means (3.85–3.95) indicated mild positive acceptance, with PU ↔ BI the strongest correlation (rho = 0.458, p < .001). The framework, instruments, and analytic toolkit offer a replicable basis for mobile DF teaching in resource-constrained settings. These are preliminary pilot findings; causal claims require a randomized controlled comparison.

Author Biographies

Krommavut Nongnuch, Rajamangala University of Technology Suvarnabhumi, Ayutthaya, Thailand

Krommavut Nongnuch is a lecturer at the Faculty of Science and Technology, Rajamangala University of Technology Suvarnabhumi, Thailand. His research interests include digital forensics education, mobile learning, and applied cybersecurity. (EMAIL: krommavut.n@rmutsb.ac.th)

Saowalak Leelawongsarote, Rajamangala University of Technology Suvarnabhumi, Ayutthaya, Thailand

Saowalak Leelawongsarote is a lecturer at the Faculty of Science and Technology, Rajamangala University of Technology Suvarnabhumi, Thailand, with interests in educational technology and instructional design. (EMAIL: saowalak.l@rmutsb.ac.th)

Tawan Khunarsa, Rajamangala University of Technology Suvarnabhumi, Ayutthaya, Thailand

Tawan Khunarsa is a lecturer at the Faculty of Science and Technology, Rajamangala University of Technology Suvarnabhumi, Thailand, with interests in information systems and mobile application development. (EMAIL: tawan.k@rmutsb.ac.th)

Suthin Kasetratanachai, Rajamangala University of Technology Suvarnabhumi, Ayutthaya, Thailand

Suthin Kasetratanachai is a lecturer at the Faculty of Science and Technology, Rajamangala University of Technology Suvarnabhumi, Thailand, with interests in network security and cybersecurity education. (EMAIL: suthin.k@rmutsb.ac.th)

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