Maryland Shared Open Access Repository
MD-SOAR is a shared digital repository platform for twelve colleges and universities in Maryland. It is currently funded by the University System of Maryland and Affiliated Institutions (USMAI) Library Consortium (usmai.org) and other participating partner institutions. MD-SOAR is jointly governed by all participating libraries, who have agreed to share policies and practices that are necessary and appropriate for the shared platform. Within this broad framework, each library provides customized repository services and collections that meet local institutional needs. Please follow the links below to learn more about each library's repository services and collections.
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Does Strategy Matter?: A Sequential Explanatory Mixed-Method Research investigating the strategic plans and adaptation of select public universities on the West Coast.
(2024) Xiong, Tou Fue; Wachhaus, Aaron; Naylor, Lorenda; Sheehan, Ivan Sascha; University of Baltimore. College of Public Affairs; University of Baltimore. Doctor of Public Administration
The higher education landscape in the United States has seen major changes in recent years. Predominately, an increase enrollment of traditionally underrepresented minority (URM1) students as evident from data within the National Center for Education Statistics (NCES2). Moreover, 4-year degree granting public universities have seen a healthy trajectory of URM enrollment compared to private for-profit and non-profit universities (NCES3). However, the degree completion and graduation rate of URM students has been stagnant over the years with minimal growth even though the enrollment has increased steadily throughout the years4. Although public universities have seen an increase in enrollment for URM students, the fact that URM students are not graduating at rates comparable to their non-URM counterparts is concerning. From a research perspective, it is critical to analyze how institutions of higher education (mainly public universities) are responding to this issue and whether they are taking actions to close any gaps.
Literatures in public administration suggest the use of strategic planning as a tool to help large organizations such as public universities identify areas of improvement (weaknesses) while gathering collective support to find solutions to these problems (Bryson & Hamilton Edwards 2017; Conway et. al 1994; Dooris et. al 2004; Ellis, 2010). The objective is for public universities to think critically about issues hindering the academic performance of URM students and in turn, attain comprehensive responses to address this issue. Given the increasing enrollment of URM students and minimal growth of graduation rates among them, the premise of this research is to analyze the strategic plans of public universities and assess the strategic stances of universities that witness an increase in graduation rate compared to and those that did not. Using the theoretical frameworks of Miles et al. (1978) and Chaffee (1985), this research will seek to explore ways public universities can be responsive to closing the graduation gap of URM students. To accomplish this, the research design for this study is guided by a sequential explanatory mixed-method approach, which allows the researcher to conduct quantitative and qualitative analysis in which the latter is use to supplement the former. The design fits the need of this research because it gives the researcher the opportunity to not only gather quantitative data by performing content analysis of strategic plans, but also adds a layer of knowledge through interviewing key decision-makers (ideally the University President, Vice-Presidents, or other key administrators) on how they identify themselves from a strategic perspective.
1 Underrepresented Minority (URM) students as defined by various institutions of higher education consist of Black/African American, Hispanic/Latino, or American Indian.
2 National Center for Education Statistics: https://nces.ed.gov/ipeds/TrendGenerator/app/trend- table/2/2?trending=column&valueCode=grand_total&f=4%3D1%3B2%3D1%3B9%3D1%3B1%3D1&rid
=65&cid=57
3National Center for Education Statistics: Student Enrollment - How many students enroll in postsecondary institutions annually? (ed.gov)
4 National Center for Education Statistics: https://nces.ed.gov/ipeds/TrendGenerator/app/trend- table/7/19?trending=column&valueCode=2&f=1%3D1%3B2%3D1%3B4%3D1%3B5%3D1&rid=49&cid
=57
Designing Multi-modal Timeline Interfaces to Make Conscious Decisions About Alcohol Intake
(University of Baltimore, 2025-02-11) Martineau, Katherine; Blodgett, Bridget (chair); Donahue, John; The University of Baltimore Yale Gordon College of Arts & Sciences; The University of Baltimore. Master of Science in Interaction Design and Information Architecture
At a time when voice and chat technologies continue to find niches in digital, what tools can be available to people who seek support with reducing alcohol intake? Recent research on smartphone sensing to track alcohol recommends taking a social context approach to address public health hazards of drinking, while many current digital services require self-reporting. Focusing on two audiences, one who wants abstinence, and one that desires to reduce drinking, it is clear that many solutions focus on an abstinence focused approach. Medical practitioners recommend solutions that take a more phased approach to reducing alcohol usage. Traditional programs to reduce drinking in digital technologies focus on cessation and quitting permanently over a phased or reflective approach to reducing alcohol intake. Understanding how communities that experience minority stress, specifically bisexual individuals, can inform ideas of how to create new social spaces that decrease interpersonal stress. What can happen with a timeline, which typically focuses on quantitative metrics, to a solution that focuses more on modulating and thinking through alcohol usage. And, as AI assistants now can get built in code with tools like Claude and ChatGPT. Traditional biometrics like breathalyzers give personalized feedback; how can this evolve into a digital interface that tracks this data over time, and in a private and safe way in a digital interface?
ADAPTIVE IE: Investigating the Complementarity of Human-AI Collaboration to Adaptively Extract Information on-the-fly
(ACL, 2025-01) Mondal, Ishani; Yuan, Michelle; N, Anandhavelu; Garimella, Aparna; Ferraro, Francis; Blair-Stanek, Andrew; Van Durme, Benjamin; Boyd-Graber, Jordan
Information extraction (IE) needs vary over time, where a flexible information extraction (IE) system can be useful. Despite this, existing IE systems are either fully supervised, requiring expensive human annotations, or fully unsupervised, extracting information that often do not cater to user`s needs. To address these issues, we formally introduce the task of “IE on-the-fly”, and address the problem using our proposed Adaptive IE framework that uses human-in-the-loop refinement to adapt to changing user questions. Through human experiments on three diverse datasets, we demonstrate that Adaptive IE is a domain-agnostic, responsive, efficient framework for helping users access useful information while quickly reorganizing information in response to evolving information needs.
Can Generative AI be Egalitarian?
(IEEE, 2024-10) Feldman, Philip; Foulds, James; Pan, Shimei
The recent explosion of “foundation” generative AI models has been built upon the extensive extraction of value from online sources, often without corresponding reciprocation. This pattern mirrors and intensifies the extractive practices of surveillance capitalism [46], while the potential for enormous profit has challenged technology organizations’ commitments to responsible AI practices, raising significant ethical and societal concerns. However, a promising alternative is emerging: the development of models that rely on content willingly and collaboratively provided by users. This article explores this “egalitarian” approach to generative AI, taking inspiration from the successful model of Wikipedia. We explore the potential implications of this approach for the design, development, and constraints of future foundation models. We argue that such an approach is not only ethically sound but may also lead to models that are more responsive to user needs, more diverse in their training data, and ultimately more aligned with societal values. Furthermore, we explore potential challenges and limitations of this approach, including issues of scalability, quality control, and potential biases inherent in volunteercontributed content.
Fair Inference for Discrete Latent Variable Models: An Intersectional Approach
(ACM, 2024-09-04) Islam, Rashidul; Pan, Shimei; Foulds, James
It is now widely acknowledged that machine learning models, trained on data without due care, often exhibit discriminatory behavior. Traditional fairness research has mainly focused on supervised learning tasks, particularly classification. While fairness in unsupervised learning has received some attention, the literature has primarily addressed fair representation learning of continuous embeddings. This paper, however, takes a different approach by investigating fairness in unsupervised learning using graphical models with discrete latent variables. We develop a fair stochastic variational inference method for discrete latent variables. Our approach uses a fairness penalty on the variational distribution that reflects the principles of intersectionality, a comprehensive perspective on fairness from the fields of law, social sciences, and humanities. Intersectional fairness brings the challenge of data sparsity in minibatches, which we address via a stochastic approximation approach. We first show the utility of our method in improving equity and fairness for clustering using naïve Bayes and Gaussian mixture models on benchmark datasets. To demonstrate the generality of our approach and its potential for real-world impact, we then develop a specialized graphical model for criminal justice risk assessments, and use our fairness approach to prevent the inferences from encoding unfair societal biases.