Cybersecurity has become ubiquitous with the exponential growth of consumer applications and Internet of Things (IoT) devices. It is a discipline which intersects other important industries such as energy, manufacturing, and healthcare. Unfortunately, the educational pace of how Cybersecurity is taught in the classroom has not kept up to date with the research pace i.e., the latest methods revolving around Artificial Intelligence (AI) driven Cybersecurity have not permeated on the education ecosystem. The focus of the paper is to provide a framework which addresses such shortcomings by continuously investigating the impact of developments in AI technology towards redesigning and enhancing the Cybersecurity curriculum in order to produce graduates that are fit for federal, state, local, and tribal governments Cybersecurity missions.

Cybersecurity has become ubiquitous with the exponential growth of consumer applications and Internet of Things (IoT) devices. It is a discipline which intersects other important industries such as energy, manufacturing, and healthcare. Cybersecurity curriculum has traditionally been taught through a mixture of technical-track and non-technical-track (policy, legal and ethical) related courses. In this paper, we chronicle our Division’s efforts to gamify the technical-track curriculum through the Escape the Classroom (ETC) paradigm. The aim of the curriculum gamification is to provide students a fun, interesting, and rewarding experience while learning Cybersecurity through the appropriate Bloom taxonomy levels. The paper discusses our approach and highlights challenges that our faculty encountered while applying ETC towards the Cybersecurity curriculum.

It is an understatement to say that teaching efficient Computer Science and Cybersecurity courses amidst the COVID19 pandemic has placed extreme pressure on faculty. In general, this is true for any academic area which has a strong hands-on component tied to the theoretical counterpart. In this paper, we chronicle and provide our approach, from the lens of St. John's University Computer Science Chairperson and the Cybersecurity Program Director, on the suitability of utilizing WebEx HandsOn-Labs tool to close the instructional gap that exists between face-to-face and fully online teaching.

This is an innovative practice full paper. The need to develop software securely cannot be over-emphasized. The changing legal and regulatory international and local landscape for software requirements is astounding. For example, the European Union's General Data Protection Regulation (GDPR), the United States' Health Insurance Portability and Accountability Act (HIPAA), the Chinese Cybersecurity laws, and the credit card industry's Payment Card Industry Data Security Standard (PCI-DSS) are all upholding higher standards for system development and deployment. Such legal and regulatory changes of necessity require modifications and updating in software development methods that must be incorporated into cybersecurity software development courses to properly prepare students for successfully working in the field. To address these and other changes within the computing field, the Accreditation Board for Engineering (ABET) recently proposed preliminary cybersecurity accreditation criteria for which fewer than 20 universities have both applied and become ABET Cybersecurity accredited. The accreditation requires maintaining continuous course improvement in the core courses including a secure software development course. This research first reports on important topics incorporated into a senior-level secure software development for cybersecurity majors. Our research then analyses student Institutional Review Board (IRB) approved surveys to learn which course components could benefit from continuous course improvements. We apply machine learning to help build categories for ABET continual improvement. Finally, we share lessons learned and plans for future work.

This Research to Practice Full Paper discusses experiences and recommendations highlighting best practices for teaching Python1in data science at higher education institutions. Python has become the language of choice for teaching data science at both graduate and undergraduate programs. From data preprocessing, pattern extraction, predictive modeling, to visualization, the language has important libraries and tools to achieve this seamlessly. In this paper, the pedagogical approaches and learning objectives achieved by a cohort of graduate students specific to using Python at all stages of the KDD (Knowledge Discovery in Databases) life cycle are highlighted. The need to do this is motivated by describing an undergraduate data mining course taught at a STEM program in Computer Science. Teaching and research experiences to build projects are also elaborated across these groups. The paper then proposes recommended practices for teaching Python at the graduate level in similar data science programs.1Python Software Foundation, [Online]. Available: https://www.python.org/

This Research to Practice Full Paper describes the research and development experience of building two brain training mobile Android applications as part of an undergraduate mobile healthcare technology course. Along with faculty mentors, project teams researched that symptoms like loss of cognition, memory, and reasoning are indicators of the early onset of dementia, Parkinson's or Alzheimer's disease. Active brain training has been shown to alleviate some of these symptoms. Using this knowledge and the skills learned in the course, student teams designed Android mobile gaming applications with cloud back-end infrastructures to train the brain. Observations about the class composition, together with units of instruction to facilitate successful project implementation and critical thinking in an ethnically diverse STEM student pool of an R3 University are described.