TY  - JOUR
AU  - Haque, A. K. M. Fazlul
AU  - Hridoy, Raihan Ahmed
AU  - Islam, Tasikul
AU  - Akter, Taslima
PY  - 2026
DA  - 2026/08/13
TI  - Simplifying Complex Wireless Intelligence and Security Education: An Outcome-Based Blended Learning Approach for Mobile Engineering
JO  - ICCK Transactions on Mobile and Wireless Intelligence
T2  - ICCK Transactions on Mobile and Wireless Intelligence
JF  - ICCK Transactions on Mobile and Wireless Intelligence
VL  - 2
IS  - 2
SP  - 56
EP  - 67
DO  - 10.62762/TMWI.2026.248090
UR  - https://www.icck.org/article/abs/TMWI.2026.248090
KW  - wireless intelligence and security (WIS)
KW  - mobile security education platform
KW  - AI-driven learning analytics
KW  - software-defined radio (SDR)
KW  - intelligent wireless training systems
KW  - outcome-based education (OBE)
AB  - The growing complexity of wireless communication systems and mobile security threats demands a new generation of engineers capable of operating at the intersection of intelligent wireless infrastructure, software-defined radio (SDR), and mobile AI. This paper proposes an intelligent mobile security education platform that integrates AI-driven learning analytics, SDR-based practical interfaces, and cloud-based wireless simulation environments within an Outcome-Based Education (OBE) and Cognitive Load Theory (CLT) framework. The platform transforms passive learners into active creators of wireless security content via Student-Generated Multimedia (SGM), while AI dashboards monitor cognitive readiness and adapt task difficulty in real time. Inclusive design extends platform accessibility through wearable biometric sensors and AR-assisted visualization of 3D wireless signal data. A pilot study was conducted with two student cohorts ($n = 13$ per group) to evaluate the SGM component of the platform within a blended learning delivery model. The active multimedia group achieved a 76.9\% pass rate (10/13) against 23.1\% (3/13) in the traditional control group, with a statistically significant and large effect ($t(24) = 3.28$, $p = 0.003$, Cohen's $d = 1.29$). These results provide preliminary evidence that the platform's intelligent, active-learning approach substantially improves training outcomes for mobile wireless security engineering. Full empirical validation of the AI analytics, AR/VR laboratory, and 5G/IoT edge-computing modules is planned for future work.
SN  - 3069-0692
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Haque2026Simplifyin,
  author = {A. K. M. Fazlul Haque and Raihan Ahmed Hridoy and Tasikul Islam and Taslima Akter},
  title = {Simplifying Complex Wireless Intelligence and Security Education: An Outcome-Based Blended Learning Approach for Mobile Engineering},
  journal = {ICCK Transactions on Mobile and Wireless Intelligence},
  year = {2026},
  volume = {2},
  number = {2},
  pages = {56-67},
  doi = {10.62762/TMWI.2026.248090},
  url = {https://www.icck.org/article/abs/TMWI.2026.248090},
  abstract = {The growing complexity of wireless communication systems and mobile security threats demands a new generation of engineers capable of operating at the intersection of intelligent wireless infrastructure, software-defined radio (SDR), and mobile AI. This paper proposes an intelligent mobile security education platform that integrates AI-driven learning analytics, SDR-based practical interfaces, and cloud-based wireless simulation environments within an Outcome-Based Education (OBE) and Cognitive Load Theory (CLT) framework. The platform transforms passive learners into active creators of wireless security content via Student-Generated Multimedia (SGM), while AI dashboards monitor cognitive readiness and adapt task difficulty in real time. Inclusive design extends platform accessibility through wearable biometric sensors and AR-assisted visualization of 3D wireless signal data. A pilot study was conducted with two student cohorts (\$n = 13\$ per group) to evaluate the SGM component of the platform within a blended learning delivery model. The active multimedia group achieved a 76.9\\% pass rate (10/13) against 23.1\\% (3/13) in the traditional control group, with a statistically significant and large effect (\$t(24) = 3.28\$, \$p = 0.003\$, Cohen's \$d = 1.29\$). These results provide preliminary evidence that the platform's intelligent, active-learning approach substantially improves training outcomes for mobile wireless security engineering. Full empirical validation of the AI analytics, AR/VR laboratory, and 5G/IoT edge-computing modules is planned for future work.},
  keywords = {wireless intelligence and security (WIS), mobile security education platform, AI-driven learning analytics, software-defined radio (SDR), intelligent wireless training systems, outcome-based education (OBE)},
  issn = {3069-0692},
  publisher = {Institute of Central Computation and Knowledge}
}