2024 DELTA Awards

In its seventh year, DELTA provided funding for five projects selected from a competitive field of 28 proposals. Each of the funded projects will deploy innovative uses of digital technology to enhance the university’s teaching and learning enterprise at both the graduate and undergraduate levels.

HealthGuard: Enhancing Cybersecurity in Healthcare through AI and VR-Driven Gamified Training

Principal Investigators: Javad Abed, Carey Business School; Anton Dahbura, Whiting School of Engineering; Shih-Chun (David) Lin, Assistant Professor, School of Medicine

Abstract: HealthGuard is a transformative cybersecurity training platform that leverages Artificial Intelligence (AI), Virtual Reality (VR), and gamification to reshape cybersecurity training for healthcare professionals. This platform utilizes generative AI to dynamically simulate a broad spectrum of cyber threats within various virtual medical environments, including a virtual hospital. The immersive VR experience extends to other scenarios such as outpatient clinics and medical laboratories, enhancing realism and engagement. Coupled with gamification elements like scoring, competitions, and progression tracking, HealthGuard ensures sustained engagement and effective skill acquisition. Designed to adapt to varying learning styles, this platform prepares a proactive healthcare workforce to effectively counter and mitigate cyber threats, safeguarding critical data and healthcare infrastructure. HealthGuard aims to set new standards in experiential learning, offering a scalable and adaptable solution for diverse high-stakes environments. HealthGuard will be accessible to a broad range of healthcare professionals, regardless of their prior technical knowledge, making it an inclusive tool that promotes widespread cybersecurity awareness and competence across the healthcare sector. This innovative approach not only prepares healthcare workers to better defend against cyberattacks but also creates a proactive culture of cybersecurity. The ultimate goal of HealthGuard is to establish a new standard in cybersecurity training that is effective, engaging, and continuously relevant, ensuring the safety of critical healthcare information systems now and in the future. Additionally, the platform’s modular design allows for seamless integration into existing or future courses, enhancing its versatility and applicability in various educational settings.

Teaching and Training Modules for Translational Neurosurgery Research

Principal Investigator: Betty Tyler, School of Medicine

Abstract: The Hunterian Neurosurgical Laboratory at the Johns Hopkins University has a successful track record of mentorship and nurturing the scientific curiosity, knowledge, and skill of young researchers in the fields of translational neurosurgery and neuro-oncology. Teaching and learning at the laboratory have been a staple of the laboratory experience for decades with emphasis on observation, supervised practice, and active involvement. Building on this experience by creating digital education opportunities in the form of interactive modules complementary to in-person laboratory training will further enable the laboratory’s commitment to disseminate scientific acumen. While prioritizing inclusivity and accessibility, the Hunterian Neurosurgical Laboratory seeks to create, maintain and continually expand a series of modules to engage countless laboratory members, Johns Hopkins University students and staff, students in surrounding Baltimore institutions, and the greater community of academics and scientists.

Collaborative Data Science Learning Environment for Learners Outside of Core-Computation Disciplines

Principal Investigator: Tom Lippincott, Krieger School of Arts and Sciences and Whiting School of Engineering

Abstract: We propose to develop and evaluate a collaborative computational learning environment that supports the integration of data science skills in humanities and social sciences courses. The revolutionary power and accessibility of modern text, image and audio machine learning and data science techniques offers transformative research possibilities to fields that have traditionally not included computation as a core discipline. However, computational learning objectives like coding and data handling are notoriously challenging skills to master for humanities majors when presented in the same format as they are in core-computational curricula. The unfamiliar nature of the computational platforms that enable their use often still present a barrier to access for those without computation as a core part of their discipline. The goal of the proposed environment is to bridge that gap and enable computational and data science skills to be accessible for noncore computational disciplines in the humanities and social sciences. A successful pilot implementation and dissemination of this model along with faculty training would have an enormous impact on the integration of computational/AI skills into humanities and social sciences curricula throughout Hopkins. The outcomes of the project enhance teaching and learning in Hopkins by developing a restricted and therefore safe technological environment to encourage student-led exploration of data and programming in applied humanities and social sciences domains. This grant will facilitate an interdisciplinary collaboration between faculty members in Alexander Grass Humanities Institute (AGHI), the University Writing Program and History (KSAS), SNF Agora Institute and the Institute for Data Intensive Engineering and Science (IDIES). The unique teaching perspectives from the collaborating departments will help us fulfill the project goals, and the infrastructural support from the IDIES ensure the sustainability of the project beyond the funding period.

Learning Analytics for Engineering Design Education: Tracking Time on Tasks to Encourage Self-Organization Skills in Undergraduate Engineering Students

Principal Investigators: Constanza Miranda and Nusaybah Abu-Mulaweh, Whiting School of Engineering

Abstract: Self-regulated learning and time management have long been essential components of design education in engineering. During the COVID-19 pandemic and the transition to online environments, self-regulated learning was one of the most desired and needed learning outcomes in higher education programs worldwide. Furthermore, time management has proven critical for students’ academic success in the distance-learning version of their courses. If anything, the pandemic showed us that, in the years to follow, self-regulated learning and time management skills will be more important than ever. Effective time management skills have correlated with academic success in MOOCs, but this has not been entirely proven in traditional college environments. On the other hand, data from the Center for Student Success shows that from 2017 to 2023, Hopkins’ students have diversified socioeconomically, culturally, and demographically. Furthermore, one of the top two reasons identified by our students from KSAS, WSE, and Peabody for not graduating within six years is physical and mental health (CSS, 2024). Our students describe a lack of study habits or “catching up” with their peers who are more equipped due to their elementary and middle school experiences. In the past, we have worked in an in-class learning analytics assessment tool for distributed design teams. With promising results and a deep reflection on the use and outreach of the instrument, this tool was undertaken by larger computing classes in our former institution. Results show that it helped the students by bringing awareness to their time organization, and it has better informed the faculty on their strategies for assessment. Our goal will be to create and pilot an automated version of time-on-task measurements in an engineering-design course. The benefit of this project looks to encourage the development of self-regulation and task-management skills in undergraduate students. Secondly, we want to create an applied framework for using learning analytics. Finally, a triangulated assessment will help us to have a pilot that can potentially be used on other project-based courses enhancing teaching and learning across the university.

Practical Magnetic Resonance Imaging for All: Learning Through Building and Playing

Principal Investigators: Sairam Geethanath, School of Medicine; Hanzhang Lu, Whiting School of Engineering; Steven Ross and Jennifer Morrison, School of Education

Abstract: Magnetic resonance imaging (MRI) is a life-saving technology that has demonstrated benefits in clinical practice and basic sciences across different diseases and anatomies. However, two-thirds of the world does not have access to MRI (1). The significant barriers to access are the cost and siting requirements of the scanner and the lack of practical MR education that is critical for its appropriate use, sustenance, and advancement. Our overarching goal is to overcome the challenge of access to practical MRI for all (‘mri4all’) through open-source hardware and software educational tools using accessible digital platforms like the internet. This project will overcome access limitations and deliver a seminal, cutting-edge curriculum using an online game-based digital platform that allows students to build and play with their own educational MR scanner. Our long-term objective is an online Coursera-like game-based course with a do-it-yourself (DIY) hardware kit available for purchase. The team has demonstrated experience in open-source hardware and software tools, MR education, accessible MRI research, and developing and quantifying educational assessments and evaluations for new STEM interventions. In this project, we will produce a DIY MRI package and game-play-based curriculum. The outcomes will be demonstrated through workshops at Hopkins for participants in the USA and in a low-resource setting like Uganda. We will also show the flexibility of the curriculum by integrating it with an ongoing undergraduate BME course at Hopkins. The project leaders from the School of Medicine will engage the School of Education’s Center for Research and Reform in Education (CRRE) to develop and conduct the formal evaluation and assessment of the proposed program’s success. The evaluators will observe two workshops and two course sessions involving MRI applications. The data analysis will be descriptive, and UG course outcomes will be compared for intervention and control students. We expect students to demonstrate i) a deeper understanding of MRI working principles from practical experience and limitations of real-world implementations, ii) an increased ability to use and contribute open-source software and hardware; and iii) increased enthusiasm and a positive attitude to learn and test STEM concepts related to MRI hardware, software, usage, and applications.