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Dissertation Defenses

Our Ph.D. students work hard for years honing their research. This effort culminates in an oral dissertation where they present, explain, and defend their ideas. The final defense of the dissertation is conducted by an examination committee recommended by the graduate faculty advisor and approved by the dean of The Graduate School. All final oral examinations are open to members of the graduate faculty.

Below is the published list of upcoming or recent dissertation defenses for Information Systems students.

Upcoming Defenses


Recent Defenses

Title: Understanding Immersion Experience when Using Virtual Reality for Foreign Language Learning
Student: Hye-Kyung Bae (Ph.D. student, Human-Centered Computing)
Date/time: Tuesday 19th, January 2021, 11am-1pm

Dr. Wayne Lutters, Information Systems, UMBC (Co-Chair)
Dr. Anita Komlodi, Information Systems, UMBC (Co-Chair)
Dr. Ravi Kuber, Information Systems, UMBC
Dr. Zane Berge, Department of Education, UMBC
Dr. Kyung-Eun Yoon, Modern Languages, Linguistics & Intercultural Communication, UMBC

One of the most effective ways to learn a foreign language is an immersion in its native cultures. While visiting another country, immersion experiences accelerate learning and confidence by dining in restaurants, shopping in markets, participating in events, and meeting new people. However, in practice, few students have access to these kinds of experiences. Virtual Reality (VR) technology has the potential to expand access allowing more students to have similar immersion experiences. These experiences are more engaging than conventional methods such as passively watching videos or interacting with two-dimensional user interfaces. However, are they more conducive to learning?

Some recent studies have dealt with VR for foreign language learning, which includes either artificial graphics-based VR simulations or naturalistic 360-degree VR videos. However, few studies have compared these two different approaches. In VR simulations participants learn vocabulary by interacting with artificial objects or agents. In contrast, 360-degree videos provide a more real-life experience, but with fewer opportunities for interaction. Both support and encourage independent movement inspiring learners to freely explore the virtual world on their own to improve their language skills in context.

This dissertation project includes three studies to understand immersion experiences in different VR conditions for foreign language learning. Study0 as an initial study to provide proof of concept for this approach was carried out using 360-degree videos with guided components. Study1 was conducted using two sets of 360-degree videos of real-life settings, comparing unguided vs. guided components. Study2 will compare 360-degree videos to VR simulations. Initial results from Study0 and Study1 have been promising, such as performing better for learning with the guidance and exploring more on their own without the guidance. The expected contributions are to provide a better understanding of students’ VR experience for foreign language learning, some guidance to people who would choose VR applications, and design implications and suggestions for those who would create VR content.


Title: Applying Virtual Reality to Motivate and Prepare International Students for Community Engagement
Student: Iman Alshathri (Ph.D. IS)
Date/time: Tuesday 12th January 2021, 10am-12pm

Dr. Anita Komlodi (Co-Chair)
Dr. Ravi Kuber (Co-Chair)
Dr. Foad Hamidi
Dr. Lina Zhou, UNCC (
Dr. Norah Abokhodair, Microsoft Learning Innovation Studio (

There are numerous benefits to students participating in community engagement activities.   However, levels of participation among international students are lower compared to their domestic counterparts. Virtual reality has been shown to have the potential to encourage behavioral change among individuals in a range of contexts.  Researchers have yet to examine the impact of virtual reality to promote community engagement among international students. Our preliminary investigations suggest that this technology offers promise to motivate and prepare international graduate students to volunteer, by supporting the changing of perceptions and attitudes towards volunteering to support community engagement initiatives. More specifically, the aim of this research is to examine the role virtual reality technology plays in encouraging international students to volunteer in a community that is different from their own.  Two studies are proposed as part of this work.  Findings will lead to the development of suggestions to support community engagement trainers who are interested in applying virtual reality to motivate and prepare individuals to volunteer.


Title: Understanding and Supporting Collaboration Between Youth in Technical Workplaces
Student: William Easley
Time: Friday, September 11, 2020, 12 pm – 3 pm
Location: Meeting Link:
Meeting number (access code): 120 111 8670
Meeting password: 3exRkqNYu34

Dr. Foad Hamidi (Chair/Advisor)
Dr. Helena Mentis (Chair/Advisor)
Dr. Anita Komlodi
Dr. Amy Hurst
Dr. Wayne G. Lutters
Dr. Betsy DiSalvo

Emerging digital technological advances have been a driving force in the evolution of the modern workplace. This influence extends not only to the creation of new industries and types of work, but also to how work is completed. For example, new technologies have made it possible for us to coordinate and communicate over vast geographic distances. In response to these changes, considerable efforts have been made to re-train our existing workforce to have the technical and communi- cation skills necessary to be successful. This shifting landscape presents exciting opportunities to focus on ways to support youth – who have spent their entire lives with access to inter-networked technologies – as they prepare to enter the workplace of the future.

This dissertation describes longitudinal research conducted at the Digital Harbor Foundation 3D Print Shop over a period of more than three years. The print shop offers after-school technical employment to youth from diverse backgrounds. To understand the experiences and challenges faced by this population as they transitioned into this environment, we have utilized a variety of methods including participant and direct observation, interviews, the analysis of chat logs, a focus group, and a participatory elicitation method.

The three studies included in this document all seek to better understand how youth coordinate work in technical settings with a specific focus on their usage of communication technologies. The first documents and unpacks instances where youth successfully and unsuccessfully coordinated their work during asynchronous handoffs. The second study characterizes the youths’ use of Slack, a popular workplace chatting tool and investigates the factors motivating and limiting adoption of this tool. The final study examines the youths’ attitudes and perceptions towards workplace communication technologies. Findings and recommendations from this dissertation offer insight into how we can better prepare and support our future workforce.


Title: Interpretable deep learning models for Electronic Health Records
Student: Peichang Shi
Advisor: Dr. Aryya Gangopadhyay
Time: Monday, June 22, 2019, 10 am – 12 pm
Location: Virtual

Deep learning neural networks (DNNs) have been proved to be powerful tools in computer vision and machine learning across robotics, healthcare, and security. However, despite their superior performance to traditional machine learning methods, it remains challenging to understand their inner mechanism and explain their downstream output due to the black box effects.

In this dissertation proposal, we aim to unfold the black-box and provide explanations in a transparent way using Electronic Health Records (EHRs). Our proposed approach is to add a partially connected layer after the output from the DNNs based on a decision tree. This decision tree layer could interpret DNNs through rules from the input vectors. Our method could achieve global interpretability for all the test instances on the hidden layers without sacrificing the accuracy obtained from the original deep learning model. This means our model is faithful to the original deep DNN model, which leads to reliable interpretations. Beyond that, we would also like to address some common issues related to EHRs including irregular time series data, censored data, multitask learning issues and hierarchical structure variables in the model.

The methods proposed in this dissertation will be validated with the End Stage Renal Disease (ESRD) Population Public Use File (PUF). This PUF data contains the current and historical ESRD patient population and associated clinical data, which has about 3.2 million patients including hospital admissions, hospitalizations, chronic conditions and 13 monthly clinical measures.