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Michelle Aebersold

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Dr. Aebersold’s professional and academic career is focused on advancing the science of learning applied in simulation to align clinician and student practice behaviors with research evidence to improve learner and health outcomes.  She focuses her scholarship in both high fidelity and virtual reality simulation and is a national leader and expert in simulation. Her scholarship has culminated in developing the Simulation Model to Improve Learner and Health Outcomes (SMILHO).

Current Research Grants and Programs:

  • Closing the loop: new data tools for measuring change in the quality for nursing education and the value of new approaches to instruction (PI) University of Michigan School of Nursing.
  • Interactive anatomy-augmented virtual simulation training (PI with Voepel-Lewis) Archie MD Award Number 045889

Profile: Building a Better Nurse

By Dan Meisler
Communications Manager, ARC

Michelle Aebersold has spent her career trying to “build a better nurse,” through training development, virtual clinical environments, and patient simulations.

As Director of Simulation and Educational Innovation at the U-M School of Nursing, Aebersold is focused on creating realistic learning environments for nursing students.

But it wasn’t until relatively recently that she realized she had an under-utilized but powerful tool at her disposal — data.

For years, Aebersold has been collecting data on how student performance is affected by participation in various simulations, but it wasn’t until speaking to faculty from the Michigan Institute for Data Science (MIDAS) that she realized the potential insights all of her historical datasets might have hidden inside.

“We’re trying to pull them all together to create a common thread, so that as we collect data on our students, we can follow them to say how they progressed. How many simulations and what type of simulations did they get and how did that impact their learning?” Aebersold said. “We hit students with a lot of simulations while they’re in school, but we don’t necessarily know which ones are better for certain things.”

Now, as MIDAS core faculty member, Aebersold will be able to easily collaborate with other MIDAS researchers to refine and analyze her data.

Eventually, she said, she’d like to be able to track nursing students as they enter their first jobs to see how their training translates to real-world performance. And a further goal for Aebersold is to help the practice of nursing education become similar to the training world-class athletes receive, in which, for example, subjects review videos of their performance to identify what works and what doesn’t.

“We’re going to introduce some things, like what can we do with virtual simulations, or what can we do with eye-tracking,” she said. “We really want to try to dive deeper, by being able to put all this information together using a lot of these data science methods.”

Aebersold has been at U-M since the mid-1980s, serving in a variety of clinical and administrative roles. She earned a Ph.D in Nursing from the University in 2008, and served as Director of the Clinical Learning Center in the School of Nursing for eight years thereafter. Since 2016, she’s been Director of Simulation and Educational Innovation.

One of Aebersold’s recent projects, “Closing the Loop: New Data Tools for measuring Changes in the Quality of Nursing Education,” applies modern data-science tools to help understand the correlation between traditional tests and student skills and competence when they enter the workforce, and whether simulations coupled with debriefing sessions translate into improvements in skills and in test outcomes.

Another potential project will focus on comparing the eye movements of novice nurses to expert nurses. Aebersold said initial findings have shown that nurses with more experience tend to focus their eyes on one thing for longer periods of time than those with less.

She said she’s enlisting help on that work from professors in the School of Information at U-M.

“By really understanding the difference between how experts do things and novices do things, you can help develop simulations that help novices get better,” Aebersold said.

She credits her father with starting her on the path to becoming a nurse; he was a police officer, and would sometimes take her to the local emergency room.

“I loved the fast pace,” she recalled, and the chance to have a real, positive impact on people’s lives.

After earning a nursing degree from Madonna University, she became a critical care nurse. She then gravitated toward supervision and administration, gaining a masters in business administration, also from Madonna University. She earned her Ph.D. from U-M while working as a nurse manager at the U-M health System.

She said all the technology, data, and innovative modes of study she’s brought together are all in service of one goal.

“For me, it all comes down to how can we make the patient care environment safer for our patients,” she said.

Stephanie Teasley

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Dr. Teasley’s research has focused on issues of collaboration and learning, looking specifically at how sociotechnical systems can be used to support effective collaborative processes and successful learning outcomes. As Director of the LED lab, she leads learning analytics-based research to investigate how instructional technologies and digital media are used to innovate teaching, learning, and collaboration. The LED Lab is committed to providing a significant contribution to scholarship about learning at Michigan and in the broader field as well, by building an empirical evidentiary base for the design and support of technology rich learning environments.

Romesh P. Nalliah

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Dr. Nalliah’s research expertise is process evaluation. He has studied various healthcare processes, educational processes and healthcare economics. Dr. Nalliah’s research studies were the first time nationwide data was used to highlight emergency room resource utilization for managing dental conditions in the United States. Dr. Nalliah is internationally recognized as a pioneer in the field of nationwide hospital dataset research for dental conditions and has numerous publications in peer reviewed journals. After completing a masters degree at Harvard School of Public Health, Dr. Nalliah’s interests have expanded and he has studied various public health issues including sports injuries, poisoning, child abuse, motor vehicle accidents and surgical processes (like stem cell transplants, cardiac valve surgery and fracture reduction). National recognition of his expertise in these broader topics of medicine have given rise to opportunities to lecture to medical residents, nurse practitioners, students in medical, pharmacy and nursing programs about oral health. This is his passion- that his research should inform an evolution of health education curriculum and practice.

Dr. Nalliah’s professional mission is to improve healthcare delivery systems and he is interested in improving processes, minimizing inefficiencies, reducing healthcare bottlenecks, increasing quality, and increase task sharing which will lead to a patient-centered, coherent healthcare system. Dr. Nalliah’s research has identified systems constraints and his goal is to influence policy and planning to break those constraints and improve healthcare delivery.

Perry Samson

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The capacity to predict student success depends in part on our ability to understand “educationally purposeful” student behaviors and motivations and the relationship between behaviors and motivations and academic achievement. My research focuses on how to collect student behaviors germane to learning at a higher granularity and analyze the relationships between student performance and behaviors.

Ultimately this research is aimed at designing and constructing an “earlier warning system” wherein student guidance is quasi-automated and informed by motivation, background and behaviors and delivered within weeks of the beginning of classes.

Jason Owen-Smith

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Professor Owen-Smith conducts research on the collective dynamics of large scale networks and their implications for scientific and technological innovation and surgical care. He is the executive director of the Institution for Research on Innovation and Science (IRIS, http://iris.isr.umich.edu).  IRIS is a national consortium of research universities who share data and support infrastructure designed to support research to understand, explain, and eventually improve the public value of academic research and research training.

One year snapshot of the collaboration network of a single large research university campus. Nodes are individuals employed on sponsored project grants, ties represent copayment on the same grant account in the same year. Ties are valued to reflect the number of grants in common. Node size is proportional to a simple measure of betweenness centrality and node color represents the results of a simple (walktrip) community finding algorithm. The image was created in Gephi.

One year snapshot of the collaboration network of a single large research university campus. Nodes are individuals employed on sponsored project grants, ties represent copayment on the same grant account in the same year. Ties are valued to reflect the number of grants in common. Node size is proportional to a simple measure of betweenness centrality and node color represents the results of a simple (walktrip) community finding algorithm. The image was created in Gephi.

Timothy McKay

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I am a data scientist, with extensive and various experience drawing inference from large data sets. In education research, I work to understand and improve postsecondary student outcomes using the rich, extensive, and complex digital data produced in the course of educating students in the 21st century. In 2011, we launched the E2Coach computer tailored support system, and in 2014, we began the REBUILD project, a college-wide effort to increase the use of evidence-based methods in introductory STEM courses. In 2015, we launched the Digital Innovation Greenhouse, an education technology accelerator within the UM Office of Digital Education and Innovation. In astrophysics, my main research tools have been the Sloan Digital Sky Survey, the Dark Energy Survey, and the simulations which support them both. We use these tools to probe the growth and nature of cosmic structure as well as the expansion history of the Universe, especially through studies of galaxy clusters. I have also studied astrophysical transients as part of the Robotic Optical Transient Search Experiment.

This image, drawn from a network analysis of 127,653,500 connections among 57,752 students, shows the relative degrees of connection for students in the 19 schools and colleges which constitute the University of Michigan. It provides a 30,000 foot overview of the connection and isolation of various groups of students at Michigan. (Drawn from the senior thesis work of UM Computer Science major Kar Epker)

This image, drawn from a network analysis of 127,653,500 connections among 57,752 students, shows the relative degrees of connection for students in the 19 schools and colleges which constitute the University of Michigan. It provides a 30,000 foot overview of the connection and isolation of various groups of students at Michigan. (Drawn from the senior thesis work of UM Computer Science major Kar Epker)

Kevyn Collins-Thompson

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Kevyn Collins-Thompson, PhD, is Associate Professor of Information, School of Information and Associate Professor of Electrical Engineering and Computer Science, College of Engineering, at the University of Michigan, Ann Arbor.

My lab explores algorithms and interfaces for intelligent information systems that can infer when and how to help people learn and discover. Examples include search engines that can deliver the right kind of personalized information at the right time, and intelligent tutoring systems that learn when and how to be most helpful in teaching a particular student. Toward these goals, I employ data-centric methods that include machine learning from interaction traces and large-scale text mining and retrieval. My current research is centered on education, but I’m also interested in mobile and health-related applications.

Rada Mihalcea

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The Language and Information Technologies (LIT) lab, directed by Rada Mihalcea, conducts research in natural language processing, information retrieval, and applied machine learning. The group specifically focuses on projects concerned with text semantics (word/text similarity, large semantic networks), behavior analysis (multilingual opinion analysis, multimodal models for deception detection, emotion recognition, alertness detection, stress/anxiety detection, analysis of counseling speech), big data for cross-cultural analysis (geotagging, understanding cross-cultural differences and worldview), educational applications (pedagogical search engines, automatic short answer grading, conversational technologies for student advising).

Several of the projects in the LIT lab are interdisciplinary, acknowledging the fact that language can be used to deepen our understanding in many different fields, such as psychology, sociology, history, and others.  Some of the ongoing projects in the lab are collaborations with psychologists and sociologists, and target a rich modeling of human behavior through language analysis, seeking answers to questions such as “what are the core values of a culture?” and “are there differences in how different groups of people perceive the surrounding world?” The lab is also actively working on multimodal projects to track and understand human behavior, where language analysis is complemented with other channels such as facial expressions, gestures, and physiological signals.

Of interest, Prof. Mihalcea was quoted in a story about sexism and today’s virtual assistants such as Amazon’s Alexa, Apple’s Siri, and Microsoft’s CortanaRefinery29.

The LIT lab conducts research that brings together techniques for natural language understanding, multimodal processing, and social media analysis.

The LIT lab conducts research that brings together techniques for natural language understanding, multimodal processing, and social media analysis.