Provost’s DELTA Teaching Forum 2020

The Next University: Academia After Pandemic

Following opening remarks from Provost Sunil Kumar, Dr. Bryan Alexander will present his keynote address. Dr. Alexander is an internationally known futurist, researcher, writer, speaker, consultant, and teacher, working in the field of how technology transforms education. His latest book, Academia Next: The Futures of Higher Education, was published in January 2020 by Johns Hopkins University Press [Purchase here].

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Building a Global-Local Connection: Developing a Framework for Online Service-Learning

2019 DELTA Grant Project  by R. Tyler Derreth, PhD, Johns Hopkins Bloomberg School of Public Health; Mindi Levin, PhD, SOURCE; and Gundula Bosch, PhD, Johns Hopkins Bloomberg School of Public Health; presenting with Mia Lamm, MSIS, MSLIS, Johns Hopkins Bloomberg School of Public Health; and Maggie Wear, PhD, Johns Hopkins Bloomberg School of Public Health

This project relies on the combined expertise of SOURCE as service-learning professionals, Center for Teaching and Learning as online instructional designers, and the R3 Graduate Science Initiative as experts in critically rigorous research methodology. We recognize the need to involve online students in civic engagement practices, and service-learning has proven highly effective. However, there remains no widely accepted or effective online service-learning pedagogy. The project will examine how to fully integrate service-learning pedagogy into an online course environment. The first goal of the project is to develop an innovative pedagogical framework that is theoretically sound, practically feasible, and leverages the advanced civic and critical thinking outcomes of service-learning with the broad access of online instructional design. Our second goal is to develop a pilot online service-learning course in the “Evidence- Based Teaching and Learning” series of the JHSPH R3 program.

The course will have students work with Baltimore community-based organizations (CBOs) to develop evaluation plans and materials for the organizations’ identified program. The team will collect data from the course and students to evaluate the efficacy of the innovative pedagogical framework, and then we will make revisions to ensure we develop a sustainable and widely applicable method of online teaching that can be effective for JHU and higher education in future years.

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Data Science for A.I. in Healthcare: An Online Course on a Multidisciplinary Topic

2019 DELTA Project by Swaroop Vedula, PhD; Anand Malpani, PhD; Gregory Hager, PhD; and Mathias Unberath, PhD of the Johns Hopkins Whiting School of Engineering; and Brian Caffo, PhD of the Bloomberg School of Public Health

Significance: Machine learning and artificial intelligence (ML & AI) techniques are rapidly becoming a staple in healthcare research, but both healthcare and engineering workforces at Johns Hopkins lag behind in their capacity to advance innovation through interdisciplinary collaboration and to assimilate it into practice. Specifically, healthcare scientists need to understand machine learning algorithms, and engineers need to learn biases in healthcare data that affect performance and utility of algorithms. Addressing these learning needs is critical to foster interdisciplinary collaboration at Johns Hopkins and to enable the School of Medicine to shape the future of healthcare innovations through engineering (Goals 2 and 3 in JHU’s 10 by 20 priorities). Our goal is to develop an online course to equip clinicians and engineers with critical thinking skills to design, analyze, interpret, and report research on ML & AI in healthcare. The primary target population for this course, for purposes of this grant, is clinical care providers, faculties, and graduate trainees (residents, clinical & research post-doctoral fellows, doctoral students, and research staff) in the School of Medicine and the Whiting School of Engineering at Johns Hopkins. While we target a specific population for this grant, we have secured preliminary approval from the Engineering for Professionals Program at JHU for MOOC development of our course, which can enhance global visibility and impact of JHU as a leader in driving healthcare innovation using technology.

Innovation and Approach: To our knowledge, none of the courses or curricula at Johns Hopkins address the comprehensive learning needs to build capacity in ML & AI in healthcare. The proposed course integrates principles from engineering and statistical sciences with two complementary parts. The first part is focused upon essential concepts of state-of-the-art machine learning methods. The second part is focused upon core concepts of design, bias, evaluation, and reporting in studies on engineering in healthcare. This course brings together educators with engineering, statistical, epidemiological, and clinical experience.

Evaluation and Expected Outcome: In individual learners, we will evaluate skill acquisition using pre- and post-tests focused on the learning objectives of the course. To quantify impact on the University’s 10 by 20 priorities, we will survey Johns Hopkins personnel completing the course to enumerate downstream effects such as incident journal club sessions they lead within their divisions, new research studies, and collaborations focused upon ML & AI in healthcare.

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The Next Generation Educator: What a 21st century da Vinci-like approach to learning means

Presented by Monica Lopez-Gonzalez, PhD of the Krieger School of Arts & Sciences

The National Academy of Sciences published the report “The Integration of the Humanities and Arts with Sciences, Engineering, and Medicine in Higher Education” (2018), highlighting the increasing concern of conflicting realities between academic “silos” and cross-disciplinary skills. Participants in this session will discuss what today’s biggest challenges of the fourth industrial revolution suggest for educating interdisciplinary and responsibly-minded thinkers and doers. The discussion will be framed by courses I have created and taught over the past decade across the university’s various schools and programs that merge the brain, computational, data, and health sciences with the arts, entrepreneurship, ethics, and storytelling.

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Cultivating Anti-Oppressive Learning Communities (CAOLC)

2019 DELTA Grant Project by Graham Mooney, PhD, Johns Hopkins School of Medicine; Keilah Jacques, MSW, CNP, CVA, Johns Hopkins School of Public Health; presenting with Anushka R Aqil, MPH; Rachel Viqueira, MHS; and Samantha Viqueira

Health disparities and health equity are not system level alone, they are the result of the ways oppression, power dynamics, and bias exist as embedded elements of teaching and learning for health professionals. Teaching practices have gone largely unchanged for more than a century. While there have been pedagogical counter-methods that introduce power and bias within curricula (Freire, 2005), these practices remain on the fringes of pedagogical work. Currently, there are few comprehensive pedagogical frameworks centering injustice orientation or designed with a bent towards anti-oppression framing (Agosto et.al., 2019). Similarly, there is an emerging need for comprehensive teaching methods for faculty that support the incorporation of multiple teaching modalities to model anti-oppressive approaches to teaching and learning (Clifford et.al., 2005; Mattsson, 2014; Metzl, & Petty, 2017). The purpose of utilizing a social justice and anti-oppressive pedagogical framework is to create efforts to address personal and structural forms of domination and subordination while guiding faculty in making clear connections for students among structural forms of oppression, learning environment, and the power and privilege they will navigate as practitioners.

The value of developing this approach in a blended online and in-person learning environment is 1) to cater to the individual needs of the learner and unique learning styles; 2) to architect innovation that can support confident integration of topics in the classroom; and 3) to guide participants through deliberate steps to develop individual learning practices necessary to advance structural competence on issues of injustice. Gamified simulation increases information retention rates (Baines & Edwards, 2015), and can strengthen the relationship between instructional content and long-term learning outcomes (Soboleva et. al., 2018). Considering this, we propose to build a technology-oriented teaching framework that facilitates a social justice pedagogy approach through using the example of hyper-segregation in Baltimore City as a foundation to understand how systematic oppression impacts health equity.

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Testing Effectiveness and Creating Tools for Automated Online Lectures

2019 DELTA Grant Project by John Muschelli, PhD, Johns Hopkins Bloomberg School of Public Health

The main medium of online learning is video lectures. However, for instructors creating video lectures takes time and requires video software skills and resources. Also, they are largely written for English-speaking audiences. We propose to set up a workflow that will allow instructors to take a set of slides, the words to be spoken on those slides, and automatically combine them into a video with the words spoken over top of the slides. We use open-source tools such as ffmpeg and R and paid services like the Amazon Polly text-to-speech API. Using other tools, such as the Google Translate API, and complementing it with human annotation, we will convert these videos into multiple different languages, each with language-specific subtitles. This allows for a set of videos to be created, each with a trackable version, while removing the need to re-record lectures. Our proposal includes: 1) an easy way for instructors to convert slides with notes to video lectures and update them, 2) converting the lectures into multiple languages, and 3) evaluating the student experience and efficacy of these lectures.

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All for One, One for All – In Addition to Accessibility, Captioning Enhances Pedagogy

Presented by Tilak Ratnanather, DPhil, Johns Hopkins Whiting School of Engineering

Early diagnosis of hearing loss and hearing technology enable more students with hearing loss to attend college which can be challenging. Fortunately, smartphones and tablets now make it possible to leverage automatic speech recognition technology, which transcribes speech to text in real time. Current technology has a 15% word error rate (WER) which drops to 5% with training which is the rate by humans. So within a few years, 5% WER will be attained which will make pedagogy accessible for everyone. We will discuss how colleges can proactively accommodate even the student who has yet to disclose mild hearing loss.

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PyRosetta Jupyter Notebooks Teach Biomolecular Structure Prediction and Design

PyRosetta is a Python library of the Rosetta computational methods for protein structure prediction and design. We currently have over 15 interactive multimedia workshops to teach topics in the field from protein structural analysis and visualization to protein folding and docking. Students can now access and learn PyRosetta with just a web browser and no local computer installation. This approach may improve engagement and retention, as well as help remove barriers to entry in the field of biomolecular structure prediction and design.

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Increasing Consistency, Community, and Quality in a Large Enrollment, Adjunct-Instructed Online Program

Presented by Ashleigh Franco and Gina Phipps of the School of Education

Successful implementation of a large-enrollment, adjunct-instructed online program requires striking a balance between developing standardized instructional and assessment practices to ensure quality teaching and learning and facilitating relationship building among instructors and candidates to enable robust community development. Attend this session to learn about how the School of Education’s Teacher Development Partnership is succeeding in striking this balance and about how our successes and lessons learned can be adapted for other online programs.

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Using Audio & Video Technologies to Foster Community & Connection in Online Doctoral Course

Presented by Carey Borkoski, Laura Quaynor and Laura Flores Shaw of the School of Education

The Ed.D. program at Johns Hopkins University School of Education is a nationally-recognized program for preparing practitioner-scholars. Within this fully online, asynchronous doctoral program with four separate specializations, faculty use a cohort model and technologies to support a robust graduation rate. In this poster, faculty supporting first year doctoral students share the strategic use of video and audio recordings as tools to bridge time and space to create community. Media produced needs to include personal connection, novelty, and requires simple to sophisticated technological and presentation skills that many faculty members have not yet developed.

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The Teach-Out Model: A New Way to Learn and Engage

Presented by Rebecca G. Williams, MSPH, Johns Hopkins Bloomberg School of Public Health; Cassandra Crifasi, PhD, MPH, Johns Hopkins Bloomberg School of Public Health

The Teach-Out model was first pioneered by the University of Michigan to spark discussion around complex social issues. Teach-Outs are an opportunity for learners around the world to come together on a digital platform to engage with subject matter experts, participate in productive discourse, and act to effect change in their personal lives and communities. The Center for Teaching and Learning and Center for Gun Policy and Research collaborated to create Johns Hopkins University’s pilot Teach-Out. “Firearm Purchaser Licensing: The Background Check Not Enough People are Talking About” launched in February 2020 yielding more than 1,300 learners within four weeks from countries around the globe.

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Improving Education and Safety in Neonatal and Infant Surgery Through Development of a Neonatal Minimally Invasive Surgery Model

2019 DELTA Grant Project by Daniel Rhee, MD, MPH, Johns Hopkins School of Medicine

Minimally invasive surgery (MIS) is an essential aspect of surgical care and a core aspect of general surgery training. Graduating surgical trainees reach sophisticated levels of MIS performance in adult surgery; however, opportunities for developing MIS excellence in neonates and infants are limited. MIS training in neonates and infants present unique challenges due to the complex nature of the operations, patient size, confined working spaces, and delicate nature of their tissues. Developing proficiency in neonatal MIS requires repeated deliberate practice and there are no adequate resources available for MIS training in this population.

We propose to develop a model of a neonate through 3D printing technology and modify it to simulate the major index operations in neonatal MIS. We hypothesize that a model that emulates the anatomy and the technical steps of these operations can significantly improve surgical trainee education and proficiency in neonatal surgery and ultimately improve patient safety. This can be accomplished through the following specific aims: 1) To develop a neonatal MIS model trainer using 3D printing that best simulates the configuration, anatomy, and primary maneuvers of neonatal congenital surgery. 2) To develop a curriculum based on the 3D model for surgical trainees to develop proficiency in MIS in complex surgery in neonates and infants.

Through collaboration between the Department of Surgery and Art as Applied to Medicine, a prototype model has already been built and successfully tested, proving that simulation of neonatal MIS techniques is feasible. Expansion of the model prototype’s current design will focus on increasing the breadth of simulated operations, simplifying its set up, and increasing its durability for repeated use. Development of the curriculum will emphasize technical skills as well as critical operative decision making. Anticipated outcomes include improvement in resident proficiency in completing simulated exercises, improved resident involvement in patient operations, and improved patient safety outcomes. Evaluation and assessment will focus on how the 3D model emulates the correct surgery, curriculum-associated improvements in surgical trainee proficiency, and improvements in patient safety through simulation.

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Getting Started with UDL: Hopkins Universal Design for Learning (HUDL) Initiative Update and Design Studio

Presented by Valerie Hartman, MS, Peabody Institute; Paul Huckett, MS, Johns Hopkins Engineering for Professionals; Lindsay Ledebur, MA, Johns Hopkins School of Medicine; Suzanne Monthie, MS, Johns Hopkins Center for Talented Youth; Donna Schnupp, MA, Johns Hopkins School of Education

This interactive session introduces how to expand variability and learner choice in your courses through universal design for learning (UDL). Learn about the Hopkins UDL (HUDL) initiative and the four areas of focus to address the diverse needs of your students. Based on neuroscience and brain-based learning, UDL fosters the development of expert learners and can even help balance your teaching workload. In the HUDL Design Studio, multiple demonstrations showcase practical examples of UDL in action around Hopkins. Get ready to explore the what, why, and how of UDL.

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Pregnancy MD: An obstetric application for patient triage and education

2019 DELTA Grant Project by Jeanne Sheffield, MD; Meghan McMahon, MD; and Anja Frost, MD of Johns Hopkins School of Medicine

The Obstetric Triage Web Application will be an easily accessible web app aimed at patients, but also available for learners and providers to address common obstetric triage concerns and to provide first line recommendations and education. Obstetric triage is an emergency department on Labor and Delivery aimed specifically to address the needs of pregnant and postpartum patients. Common chief complaints include contractions, abdominal pain, decreased fetal movement, vaginal bleeding, and many others. According to the American College of Obstetrics and Gynecology (ACOG), the obstetric triage volume exceeds the overall birth volume of a hospital by 20-50%, and another large study showed only 36% of patients present to triage because they believe there is a true emergency. With these large volumes of non-emergent complaints, patients, providers, and the healthcare system suffer. Patients can experience long wait times, high triage bills, and dissatisfaction. Providers have increased patient demands and time away from more critical patient needs. Ultimately this results in increased health care visits and costs. Our obstetric online web app would address the most common obstetric triage scenarios to provide home remedies, expert recommendations, and resources and education to help alleviate this healthcare system burden. This will be a novel extension of digital learning for patients and providers in the obstetric population who frequently access applications and technology for health information, which has been an increasingly popular trend amongst all fields of medicine in the last decade. While multiple various obstetric applications exist, this would be the first to offer more individualized and stratified recommendations for complaints and concerns during and after pregnancy and offer a significant concurrent educational component.

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Structural and Strategic Elements for Successful Implementation of Flipped Class Designs

Presented by Alison A. Papadakis, PhD; Meghan McGlaughlin, MA; Carolina Daffre, BA; and Emily Franco of the Johns Hopkins Krieger School of Arts & Sciences

Meta-analyses demonstrate flipped courses have more student engagement, learning, and diverse assessments than traditional courses, but the results are variable across courses and fields, suggesting the need for more adaption. We will share a highly adaptable flipped course structure that includes live and online lectures and small group sections without increased instructor time. In our large enrollment psychology course, students preferred the flipped design; gave higher ratings on opportunities for concept application and critical analysis; and had better exam performance for targeted skills. This presentation will facilitate adaption of flipped designs to attendees’ own courses.

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Scaling Up Digital Qualitative Data Analysis

Presented by William Marcellino, PhD of the Pardee RAND Graduate School

Qualitative data analysis is critical in developing causal explanations and understanding complex social problems. Learning by-hand qualitative analysis leverages student’s precise, high-context attention, but is limited in reliability, and accuracy for identifying diffuse patterns. These problems accelerate as datasets become ever larger. Analytic software that leverages human and machine strengths can improve rigor and analytical insight, giving students a more robust methodological toolbox. This session explores the benefits and challenges in using two web-based analytic platforms in the classroom: Dedoose (collaborative mixed method environment) and RAND-Lex (scalable text/social media).

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Stephen Gange, Executive Vice Provost for Academic Affairs, and keynote presenter Dr. Bryan Alexander will have a live dialogue to discuss participant questions.

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