This page contains descriptions for special topics offered at SILS (regularly offered courses are listed separately). Special topics courses are developed to cover emerging issues or specialized content not represented in the main curriculum. Not all courses are offered each semester—see the course schedule for availability.
Fall 2023:
INLS 690-166: Public Libraries and Their Communities; TBA (3 credits)
Explores the intricate relationship between public libraries and their communities, including the analysis of public library contributions to the prosperity and well-being of those they serve and how these contributions may be described and communicated most effectively.
INLS 690-212: Audio-Visual Archives Management; Weiss, Steven (1.5 credits)
An introduction to the management of audio, film and video archives with an emphasis on the history of recording, best practices for preservation and access, and copyright. Through selected readings, lecture, class discussion, assignment, and hands-on demonstration, students will gain an understanding of the history of recording, format identification, storage and handling, philosophy of media preservation and copyright.
INLS 690-270: Data Mining Methods and Applications; Wang, Yue (3 credits)
Recent years have witnessed explosive growth of data generated from myriad sources, in different formats, with varying quality. Analyzing information and extracting knowledge contained in these data sets becomes challenging for researchers and practitioners in many fields. Automatic, robust, and intelligent data mining techniques become essential tools to handle heterogeneous, noisy, unstructured, and large-scale data sets. This is a graduate-level course focused on advanced topics in data mining. It provides an overview of recent research topics in the field of data mining. It takes a data-centric approach by surveying the state-of-the-art methods to analyze (or mine) different genres of data: item sets, matrices, sequences, texts, images, networks, and more. The course will emphasize the practical aspects of data mining methods and their applications, instead of the theoretical aspects of statistical machine learning and numerical optimization. The course materials will focus on how the information in different real-world problems can be formulated as particular genres, and how the basic mining tasks of each genre of data can be accomplished. To this end, the course is suitable not only for students who are doing research in data mining related fields, but also for students who are consumers of data mining techniques in their own disciplines, such as natural language processing, information retrieval, human computer interaction, social computing, health informatics, informetrics, digital humanities, economics, and business intelligence.
INLS 690-276: Information Professionals in the Makerspace; Melo, Marijel (3 credits)
Despite the increasing popularity of makerspaces across the U.S., there still remains little formal preparation or classroom training for burgeoning information professionals to design and run makerspaces. This course seeks to narrow that gap with a curriculum dedicated to making, makerspaces, and the information professional. In this course, students will critically engage with the conceptualization of the makerspace in a “T-shaped” manner. Vertically, students will develop a deeply practical, critical, and theoretical understanding of the makerspace and its remarkable adoption rate in libraries and communities across the U.S.; horizontally, students will develop a wide-range of technical skills in areas such as fabrication (laser cutting, 3D printing, and sewing), circuitry (paper circuits, electronic textiles, and soldering), extended reality (XR: AR/VR/MR), and micro-computing. At the end of the course, students will have engaged with a variety of topics including how to staff a makerspace, equity and inclusion, and ways to navigate ethical issues.
INLS 690-308: Philosophy and Ethics of AI; Fox, Michael (3 credits)
This course covers such topics as the Turing test and the frame problem in the philosophy of AI,
superintelligence and the so-called singularity—when AI becomes uncontrollable and
independent, when it governs and improves itself—the existential risk of Artificial General
Intelligence (AGI), the moral status of AI, the design of ethical AI, and the possible implications
of merging AI with humans. We will engage with leading philosophers and ethicists in the field,
reflect on stories by Isaac Asimov and Harlan Ellison, and critique the films 2001: A Space
Odyssey and Her. We will also explore what some consider to be myths about AI. Overall, this
course will teach you how to think critically about AI from philosophical, ethical, and
imaginative perspectives.
INLS 690-322: The Art and Science of Readers Advisory ; Speace, Gillian (3 credits)
This course offers an introduction to readers' advisory, incorporating both traditional approaches and technological solutions to the challenge of helping readers find books. Students will learn best practices for providing effective readers' advisory service to patrons in a variety of contexts and become familiar with specialized RA resources and tools.
INLS 890-001: Professional Seminar in IS Research and Teaching; Lee, Christopher (1.0 credit)
This course addresses various aspects of academic life for doctoral students. Themes and topics vary by term, based on student interests and needs. Possible topics include, but are not limited to, work/life balance; selecting publication venues; working with one’s advisor and committee; time management and productivity strategies; the publication peer review process (as author or reviewer); setting career goals; pursuing and applying for jobs; and the grant application process (as submitter or reviewer). NOTE: Doctoral students only and must be ABD (finished with coursework but not yet completed their comprehensive exams and dissertation prospectus).
Spring 2023:
INLS 490-289: Social Problems in an Information Society; Cottom (3 credits)
This course will survey social, political, economic and ethical issues in an information-based society.
INLS 690-270: Data Mining Methods and Applications; Wang (3 credits)
Pre-reqs: INLS 560 and one or more of the following classes; 509, 512, 613 and 625. Recent years have witnessed explosive growth of data generated from myriad sources, in different formats, with varying quality. Analyzing information and extracting knowledge contained in these data sets becomes challenging for researchers and practitioners in many fields. Automatic, robust, and intelligent data mining techniques become essential tools to handle heterogeneous, noisy, unstructured, and large-scale data sets. This is a graduate-level course focused on advanced topics in data mining. It provides an overview of recent research topics in the field of data mining. It takes a data-centric approach by surveying the state-of-the-art methods to analyze (or mine) different genres of data: item sets, matrices, sequences, texts, images,
networks, and more. The course will emphasize the practical aspects of data mining methods and their applications, instead of the theoretical aspects of statistical machine learning and numerical optimization. The course materials will
focus on how the information in different real-world problems can be formulated as particular genres, and how the basic mining tasks of each genre of data can be accomplished. To this end, the course is suitable not only for students who are doing research in data mining related fields, but also for students who are consumers of data mining techniques in their own disciplines, such as natural language processing, information retrieval, human computer interaction, social computing, health informatics, informetrics, digital humanities, economics, and business intelligence.
INLS 690-282: Community Archiving; Kuecker (3 credits)
A number of archivists are advocating for a new, collaborative model of archiving that empowers communities to look after their own records “by partnering professional archival expertise with communities’ deep sense of commitment and pride in their own heritage and identity.” This class will explore the many ideas and issues surrounding this new model by working with a local community group to develop a comprehensive strategy for collecting, describing and maintaining their historical records in both analog and digital formats. The work will be informed by discussion of relevant literature and examination of other community archiving projects.
CHIP 490-291: Health Care Systems in the US; Potenziani (3 credits)
This course is meant to introduce students to the breadth and complexity of the US health care system. We will look at the functioning parts of how and who delivers health care beginning with a global perspective to provide some context. Then we will examine the institutions where care is delivered, the people who provide those services, and the medicines, devices and technologies involved. All of these exist within, outside, and strongly influenced by government policies and programs from the federal level to the states and down to local jurisdictions. With this basis in hand, then we turn our attention to financing all the above—a sector of the domestic economy that sees $3.5 trillion dollars spent each year—more per capita than any country on earth, by far. We will examine what outcomes we get for our money and methods used to assess system performance. All the above in the first half of the semester. After a summative Project Assignment delivered at mid-term (more on that below), we will return to explore the development of the health care system we have today. We will trace the roots of our system to understand the why undergirding its form, financing, and operation. We will finish with an examination of the current state of affairs in light of extant federal policy. Our final episode will be an examination of the emergence, response, and outcomes to date of the COVID-19 pandemic in the US, looking at our chosen counties this time as laboratories of practice.
CHIP 490-296: Systems Analysis in Healthcare; Sharmin (3 credits)
Introduction to the systems approach to the design and development of health information systems. Understanding systems analysis in healthcare. Methods and techniques for the analysis and modelling of system functionality (e.g., structured analysis) and data represented in the system (e.g., object-oriented analysis) are studied.
CHIP 490-297: Database Systems in Healthcare; Tweedy (3 credits)
Students will learn the basics of setup, administration, and querying relational databases using SQL to perform CRUD operations: Create read Update Delete. We will also cover some basic information about indexing and normalization to improve performance, efficiency and stability.
CHIP 490-309: Quality Improvement in Data Visualization in Healthcare, Arangdad (3 credits)
In this course students will learn the basic concepts of quality improvement with special emphasis on healthcare applications. We will use many examples from the Institute for Healthcare Improvement, leading hospitals, and other healthcare organizations. This course will cover many other current leading quality management practices including continuous quality improvement, Lean, and Six Sigma. Students will get hands on experience using some data visualization tools with the focus on Tableau software. Students will get an understanding of Tableau's fundamental concepts and features: how to connect to data sources, use Tableau’s drag-and-drop interface, and create compelling visualizations.
CHIP 490-311: Usability Evaluation Methods; Sharmin (1.5 credits)
CHIP 490-312: Health Application Development and FHIR; Tweedy (1.5 credits)
Students will learn about the Fast Health Interoperability Resources standard and how to develop freestanding and embedded applications that leverage it to work with APIs and demo EHR environments using languages like JavaScript and Python.
CHIP 490-314: Quality Improvement and Lean Six Sigma; Arangdad (3 credits)
Statistical Engineering and a systematic approach to problem solving Lean Six Sigma philosophy (DMAIC) for improving healthcare and business processes using advanced graphical and statistical models. Defining the improvement opportunity, measurement system analysis, data collection, statistical analysis, design of experiment (DOE) methods, and statistical process control (SPC) methods. Applications of statistical engineering to healthcare case studies.
Fall 2022:
CHIP 490-261: Healthcare Systems in the US; Potenziani (3 credits)
This course will introduce students to the breadth and complexity of the US health care system. We will look at the functioning parts of how and who delivers health care beginning with a global perspective to provide some context. Then we will examine the institutions where care is delivered, the people who provide those services, and the medicines, devices and technologies involved. All of these exist within, outside, and strongly influenced by government policies and programs from the federal level to the states and down to local jurisdictions. With this basis in hand, then we turn our attention to financing all the above— a sector of the domestic economy that sees $3.5 trillion spent each year—more per capita than any country on earth, by far. We will examine what outcomes we get for our money and methods used to assess system performance. These developments will be examined in a selection of states and counties offering a cross-section of the nation. We will then explore the development of the health care system we have today by tracing the roots of our system to understand the "why" undergirding its form, financing, and operation. The course will include two group projects to study the health care system at the county level and then examine those jurisdictions' experience with the Covid-19 pandemic.
CHIP 490-296: Systems Analysis in Healthcare; Sharmin (3 credits)
Introduction to the systems approach to the design and development of heath information systems, understanding systems analysis in healthcare. Methods and techniques for the analysis and modelling of system functionality (e.g., structured analysis) and data represented in the system (e.g., object-oriented analysis) are studied.
CHIP 490-297: Database Systems in Healthcare; Tweedy, Jon (3 credits)
Students will learn the basics of setup, administration, and querying relational databases using SQL to perform CRUD operations: Create read Update Delete. We will also cover some basic information about indexing and normalization to improve performance, efficiency and stability.
CHIP 490-307: Human Factors in Healthcare; Mosaly, Prithima (3 credits)
Introduction to human performance and limitations in the design of effective and efficient healthcare systems, an area where it is vital to limit mistakes because human error can affect patient safety. This course is a broad survey of human factors engineering theory and methods to the analysis and improvement of healthcare delivery systems. Case studies are used to identify the human role in accidents and to identify design improvements.
CHIP 490-309: Quality Improvement Data Visualization in Healthcare; Rezaei-Arangdad; Shaghayegh (3 credits)
You will learn the basic concepts of quality improvement with special emphasis on healthcare applications. In this course we will use many examples from the Institute for Healthcare Improvement, leading hospitals, and other healthcare organizations.This course will cover many other current leading quality management practices including continuous quality improvement, Lean, and Six Sigma. You will get hands on experience using some data visualization tools with the focus on Tableau software. You will get an understanding of Tableau's fundamental concepts and features: how to connect to data sources, use Tableau’s drag-and-drop interface, and create compelling visualizations.
CHIP 490-310: Data Analytics in Healthcare; Mehndiratta, Payal (1.5 credits)
This course will provide an overview of Data Analysis and reporting, via BigQuery. The course will review several use cases in healthcare. The course is project-oriented and will require students to understand the requirements before analyzing data for design or reporting purposes with a focus on testing the data. While no specific courses are considered pre-requisites, students should have an understanding of SQL. Prior experience in excel and SQL is strongly recommended.
INLS 490-276: Real Time Data Science in the Makerspace; Melo/ Rajaskear (3 credits)
Data science is changing the way we do science, business and even everyday life. Data science spans multiple disciplines, and applies scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. In this course, we are particularly interested in collecting and applying real-time data to gain insights and knowledge about environmental and ecological phenomenon around us. Realtime data from multiple sensors can be fused and analyzed to get a deeper understanding of micro-climates including those in and around buildings and communities. In this course, students will posit research questions about enviro-sensing, build sensor systems for gathering multiple-types of real-time data in order to test their hypothesis. Students will develop or refine existing skills working with fabrication technologies often found inmakerspaces such as 3D printing, laser cutting, and sewing. Students will also gain experience in managing data
life-cycles by developing data pipelines to gather, store, and analyze data to effectively extract and visualize useful
information.
INLS 490-303: Learning Design: Digital Media, New Tools and Technology; Bhattacharya (3 credits)
This project-based course focuses on the theoretical and practical applications and issues related to designing digital media (e.g. videos, animations, podcasts, infographics) and environments (e.g. interactive websites, learning management systems, online courses) for training and development. In this course, we will explore various models of Instructional Design, learning theories, multimedia design principles, and effective pedagogical strategies and apply this knowledge to the design of educational digital media.
Course objectives:
• How use technology to develop instructional materials to enhance online learning.
• How to engage SMEs to collaborate, design and evaluate learning experiences and programs.
INLS 690- 109: Scholarly Communication; Hemminger (3 credits)
Addresses how scholarship is communicated, shared, and stored. Includes scholars approach to academic work; social relationships within academia; external stakeholders in the scholarly communication system; and emerging technologies' impact upon work practices. Topics covered include academic libraries and presses, publishing, serials crisis, open access, peer review and bibliometrics.
INLS 690-222: Social Informatics; Jarrahi (3 credits)
Understanding technological innovation, computerization, and the context of technology use are central to comprehending the social fabrics of our today's society. The object of this course is to explore different conceptualizations of how technology plays a role in shaping organizations, communities, and societies. We will draw on multiple theoretical developments from the field of Science and Technology Studies (e.g., actor-network theory and social construction of technology), social informatics and computer-supported cooperative work to enable a better understanding of the interaction between humans and technology at personal, organizational and societal levels.
This course involves reading, reflecting and discussing some classic sociotechnical work, along with research emphasizing the application of a sociotechnical perspective in understanding the role of information and communication technologies, and knowledge communities. This course helps [graduate] students gain a broad and conceptual understanding about sociotechnical issues and theories. More specifically it enables students to understand and explain the complexities inherent in the context of technology use and the many ways individuals, groups, organizations adopt and relate to ICTs
INLS 690-270: Data Mining Methods and Applications; Wang (3 credits)
Recent years have witnessed explosive growth of data generated from myriad sources, in different formats, with varying quality. Analyzing information and extracting knowledge contained in these data sets becomes challenging for researchers and practitioners in many fields. Automatic, robust, and intelligent data mining techniques become essential tools to handle heterogeneous, noisy, unstructured, and large-scale data sets.
This is a graduate-level course focused on advanced topics in data mining. It provides an overview of recent research topics in the field of data mining. It takes a data-centric approach by surveying the state-of-the- art methods to analyze (or mine) different genres of data: item sets, matrices, sequences, texts, images, networks, and more. The course materials will focus on how the information in different real-world problems can be formulated as particular genres, and how the basic mining tasks of each genre of data can be accomplished. To this end, the course is suitable not only for students who are doing research in data mining related fields, but also for students who are consumers of data mining techniques in their own disciplines, such as natural language processing, information retrieval, human computer interaction, social computing, health informatics, bibliometrics, digital humanities, economics, and business intelligence.
INLS 690-276: Information Professionals in the Makerspace; Melo (3 credits)
Despite the increasing popularity of makerspaces across the U.S., there still remains little formal preparation or classroom training for burgeoning information professionals to design and run makerspaces. This course seeks to narrow that gap with a curriculum dedicated to making, makerspaces, and the information professional. In this course, students will critically engage with the conceptualization of the makerspace in a “T-shaped” manner. Vertically, students will develop a deeply practical, critical, and theoretical understanding of the makerspace and its remarkable adoption rate in libraries and communities across the U.S.; horizontally, students will develop a wide-range of technical skills in areas such as fabrication (laser cutting, 3D printing, and sewing), circuitry (paper circuits, electronic textiles, and soldering), extended reality (XR: AR/VR/MR), and micro-computing. At the end of the course, students will have engaged with a variety of topics including how to staff a makerspace, equity and inclusion, and ways to navigate ethical issues.
INLS 690-290: Misinformation and Society; Tripodi (3 credits)
How do we know what we think we know? This course will examine the concept of mis-dis-mal-information over time - what it is, when it occurs, and how ideas of "truth" and "facts" are connected to the social construction of knowledge more generally. Readings and assignments will consider the sociotechnical dimensions of misinformation, examining how/why false narratives are created, believed, shared, and used for political gain. Finally, we will study the democratic implications of problematic content with an emphasis on understanding the ideological nature of falsehoods. Through a contextualization of misinformation, we will work toward proposing possible solutions for how we might fix or combat an ever-present and evolving problem.
INLS 690-308: Introduction to Digital Humanities; Fox (3 credits)
Computer technology has revolutionized the humanities. It has enabled scholars and practitioners to imagine what they don’t know in ways otherwise unimaginable. This course will teach you how and will provide you with the knowledge to embark on your own digital humanities projects, no matter your discipline. It will proceed through five parts. In Part One, you will acquire an intellectual grounding in the theory, history, and practice of digital computing. Understanding digital computing and its historical context is essential to beginning to understand the digital humanities, the “humanities” part of which you probably already grasp. Part Two will introduce you to some current definitions, theories, and critiques of the digital humanities. Part Three will cover the theory and practice of the first of three main facets of the digital humanities, digital editing and curation, arguably the foundational one. In Part Four, you will learn about the second main facet, data analytics in the humanities. It will include readings in visualization and in linguistic, spatial, temporal, and visual data analytics. Part Five will feature a few topics in the third main facet, the culture of computation, including criticism of digital materials, embodiment and the sense of history in the information and digital age, and artificial intelligence and its limitations, a topic which rounds off the history of digital computing and which is increasingly becoming a part of culture.
INLS 890-141: Digital Curation Workflows; Lee (3 credits)
We'll explore fundamental aspects of digital representation and implications for digital curation workflows, including common tasks and software options. Students will learn and apply methods to formally document workflows (existing and aspirational) in specific institutional settings..
Spring 2022:
INLS 490-186: A Digital Gazetteer of North Carolina (Shaw); 3 credits
Join a cooperative effort to build a digital gazetteer of North Carolina places. Our goal is to help the people of North Carolina communicate about the places they live in, visit, and study. We will do this by designing, building, and maintaining place-based digital infrastructure for the UNC System and other public institutions of North Carolina. You’ll develop experience in planning and implementing a state-of-the-art system for collecting, cleaning, enriching, and publishing data. If we’re successful, the public infrastructure we create will enhance our ability to find, understand, share and remember the places we care about.
CHIP 490-261: Health Care Systems in the US (Potenziani); 3 credits
This course will introduce students to the breadth and complexity of the US health care system. We will look at the functioning parts of how and who delivers health care beginning with a global perspective to provide some context. Then we will examine the institutions where care is delivered, the people who provide those services, and the medicines, devices and technologies involved. All of these exist within, outside, and strongly influenced by government policies and programs from the federal level to the states and down to local jurisdictions. With this basis in hand, then we turn our attention to financing all the above. We will examine what outcomes we get for our money and methods used to assess system performance. These developments will be examined in a selection of states and counties offering a cross-section of the nation. We will then explore the development of the health care system we have today by tracing the roots of our system to understand the "why" undergirding its form, financing, and operation. Finally, we will look at a special topic across the selected states and counties to see the health care system in action. The course involves both individual and small group effort. Assignments include essays, infographics, quizzes, and student presentations.
CHIP 490-296: Systems Analysis in Healthcare (Sharmin); 3 credits
Introduction to the systems approach to the design and development of health information systems. understanding systems analysis in healthcare. Methods and techniques for the analysis and modelling of system functionality (e.g., structured analysis) and data represented in the system (e.g., object-oriented analysis) are studied.
CHIP 490-297: Developing with FHIR (Tweedy); 1.5 credits
Students will learn about the Fast Health Interoperability Resources standard and how to develop freestanding and embedded applications that leverage it to work with APIs and demo EHR environments using languages like JavaScript and Python
CHIP 490-298: NLP Applications in Health Care (Shoenbill); 3 credits
This course will provide an introduction, historical perspective, and overview of natural language processing in healthcare. Content will include:
• Discussion of language and concepts relevant to NLP such as named entity recognition, corpus construction, relation extraction, types of NLP models, and methods of NLP model evaluation
• Discussion of current applications of NLP to healthcare research
• Discussion of bias and transparency in NLP models
• Discussion and demonstrations of UNC tools available to assist with NLP project design and execution such as i2b2, EMERSE, CLARK
• Discussion of off the shelf tools available for NLP project execution
INLS 690-245: Data Criticism (Feinberg); 3 credits
In this course, we look at data from the perspective of reading and writing, rather than engineering or mining. We contemplate a data criticism that aligns with other forms of criticism focused on products of human endeavor, such as literary criticism or music criticism and ask: what might a data criticism look like, and how might we go about it? Projects may include close readings of small datasets, critical design prototypes, or values-focused critiques. (counts as a class in the information bin)
CHIP 690-295: Foundations of Clinical Data Science (Pfaff); 3 credits
With the advent of Electronic Health Records (EHR), there are more opportunities than ever to make real impact on patient health and the science of medicine using data. However, clinical data has unique characteristics and structures that can make it both challenging and rewarding to use in analytics. In this course, students will gain understanding of clinical data collection, models, context, and caveats through lecture and hands-on activities. Students will then apply that knowledge to real clinical data, and use Python and other tools to perform analyses and replicate findings from literature. There are no formal prerequisites for this course, but students are expected to either come in with the ability to use SQL and Python, or should be willing to take online courses in those languages prior to the start of this course.
