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Online communities are important to our cultural, social, and economic lives and especially to how we find and share information. Yet they also threaten our well-being and may undermine critical social institutions as well as the integrity of public discourse. This course is an interdisciplinary inquiry that seeks to understand online communities. It covers the history of online communities from their origins in the pre-Internet to the rise of social media platforms and contemporary challenges and also the social, psychological, and human-computer interaction research that both explains the practical barriers to building an online community and motivates technical and organizational designs that aim to overcome them.

Introduction to Explainable AI through practical applications and real-world examples. Students will gain a basic proficiency in implementing Explainer Algorithms to explain the decisions of ML and AI systems, as well as interpreting the results in a transparent and understandable manner to a non-technical audience. The course will focus on Post-Hoc Explainability techniques and algorithms, including Feature Attribution, Rule-Based and Counterfactuals. Another focus area will be on evaluating the performance of explainability techniques, and understanding the trade-offs associated with various methods. This course is geared towards students interested in a hands-on approach to developing explanations for black-box ML and AI Systems.

This is a survey course covering the basics of the informatics professions. We cover some history of informatics as an academic discipline and profession, review some of the significant concepts that students can expect to cover later in the Informatics major and minor, and review the different concentrations available to Informatics students. Assessment in this course is conducted through weekly quizzes and discussion questions; a short, persuasive, group presentation; and a longer-term persuasive essay project with both individual and group components.

This class offers students hands-on practice with Canvas and other digital tools in order to scaffold success in the informatics program. Students will conduct an independent, iterative research project including the following steps: crafting a research question, finding and evaluating sources, and presenting information. Students will also synthesize their knowledge gained into a group report and a separate independent portfolio.

Survey the ethical foundations for informatics, incorporating non-Western and feminist perspectives. Apply these ethical foundations to contemporary problems in informatics. Examine the confrontation of ethical dilemmas in the workplace, including recognizing value trade-offs, affected stakeholders, and potential solutions. Offered on the letter-grade basis only.
This course carries the Writing Flag and the Ethics flag
Ethics courses are designed to equip students with skills that are necessary for making ethical decisions in their adult and professional lives. Students should therefore expect a substantial portion of their grade to come from assignments involving ethical issues and the process of applying ethical reasoning to real-life situations.

This class offers an introduction to computer programming for those without any prior knowledge or experience in computer programming. If you already have programming experience, please explore options below for waiving the requirement to take this class. We will introduce four broad areas related to success in computer programming: language, software engineering concepts, programming environment, and practical know-how.

In this age of information, we repeatedly hear the phrase “Do your research!”, but what does that mean? And more importantly, why does it matter? This course is broken into two parts designed to provide a foundation to begin answering these questions: 1. The first half of the course is an introduction to research, exploring what research is, what research can look like within the field of Informatics, and how Informatics research can be leveraged for social good. 2. The second half of the course dives deeper into “research methodology,” how research is done and some of the most common tools we use to explore the field’s most burning questions. Students will learn about the functions of qualitative and quantitative methods, as well as how the pieces of the research process fit together to explore challenges and potential solutions by applying human-centered values to the intersections of information, people, and technology. This course is held in-person and rather than exams, assignments are designed to encourage students to apply course concepts to their own interests. Students will leave this course empowered as citizens to critically evaluate research in terms of process, ethics, and equity.

We will describe data using visual and numerical descriptions (visualization and summary statistics). We will learn to make predictions and draw inferences using simple and multiple linear regression. We will learn classification using logistic regression. We will learn how to interpret diagnostic plots that accompany linear models. We will practice all these things using R and RStudio, which will be taught as part of the class. Some math and programming is not required but will be helpful in reducing the workload in the class. A statistics course is required but other statistics courses can be substituted for this one. This course counts for the Quantitative Reasoning flag, starting in Fall 2024.

Offered on the letter-grade basis only.

In this class, students will first learn some fundamentals of cultural heritage informatics and be introduced to the major kinds of institutions in this space: galleries, libraries, archives, and museums. Students will also see case studies of how fundamental concepts like access or metadata get used in contemporary examples.

I310D- Introduction to Human-Centered Data Science is a survey course that introduces students to the theory and practice of data science through a human-centered lens, with emphasis on how design choices influence algorithmic results. Students will gain comfort and facility with fundamental principles of data science including (a) Programming for Data Science with Python (b) Data Engineering (c) Database Systems (d) Machine Learning and (e) Human centered aspects such as privacy, bias, fairness, transparency, accountability, reproducibility, interpretability, and societal implications. Each week’s class divided into two segments: (a) Theory and Methods, a concise description of theoretical concept in data science, and (b) Tutorial, a hands-on session on applying the theory just discussed to a real-world task on publicly available data. We will use Python for programming and cover Python basics in the beginning of the course. For modules related to databases, we will use PostGre SQL.

Explore the leveraging of data, information, and technology for the greater benefit of society and to help ensure a level playing field for everyone in the information age. Offered on the letter-grade basis only.
This course carries the Cultural Diversity in the United States flag.
The purpose of the Cultural Diversity in the United States Flag is for students to explore in-depth the shared practices and beliefs of one or more underrepresented cultural groups subject to persistent marginalization. In addition to learning about these diverse groups in relation to their specific contexts, you’ll also reflect on your own cultural experiences.

Explore designing and implementing information technologies to improve healthcare delivery, healthcare management, and health outcomes. Offered on the letter-grade basis only.

An introduction to sociotechnical perspectives on information systems, their effects, and how we intervene to make them better.

This course introduces students to foundational knowledge, methods, and skills for designing human-centered user experience (UX) around interactive systems. Students will become familiar with user research, concept generation, design methods, and user evaluation. In addition, students will also learn how to collaborate in a team setting, communicate design rationales, and present compelling narratives about their work. The class will be structured with lectures, hands-on design activities such as design critiques, projects, and presentations.

The Applied Cybersecurity Community Clinic is a two-semester sequence that first equips students with the technical and business skills of an entry-level cybersecurity analyst (semester 1) and then partners them in (supervised) teams with a small local business, municipal government, nonprofit to render pro bono cybersecurity services (semester 2). During the first semester, students will learn key cybersecurity defense concepts and skills, such as vulnerability assessment, network configuration and security, access controls, authorization techniques, responding to a cyberattack, business planning, and penetration testing. Students will also learn how to form an effective cybersecurity operations team and communicate with organization and business leaders and employees about essential cybersecurity controls and functions. By the conclusion of this course, students will be prepared to work within their assigned teams to assess, design, and render a cybersecurity improvement project plan for their client organization next semester.

The Applied Cybersecurity Community Clinic is a two-semester sequence that first equips students with the technical and business skills of an entry-level cybersecurity analyst (semester 1) and then partners them in (supervised) teams with a Central Texas-based small business, municipal government, or nonprofit to render pro bono cybersecurity services (semester 2). During the first semester, students will learn key cybersecurity defense concepts and skills, such as vulnerability assessment, network configuration and security, access controls, authorization techniques, responding to a cyberattack, business planning, and penetration testing. Students will also learn how to form an effective cybersecurity operations team and communicate with organization leaders and employees about essential cybersecurity controls and functions. During the second semester, students work within their assigned teams to assess, design, and render a cybersecurity improvement project plan for their designated client organization, building cybersecurity capacity and bolstering the client organization’s ability to recover from a cyber incident long-term.

Offered on the letter-grade basis only.

Engage in modern ethical dilemmas within archives, libraries, and museums, considering issues of collections management and preservation within changing cultural frameworks. This I 320C topic carries the Cultural Diversity in the United States flag. The purpose of the Cultural Diversity in the United States Flag is for students to explore in-depth the shared practices and beliefs of one or more underrepresented cultural groups subject to persistent marginalization. In addition to learning about these diverse groups in relation to their specific contexts, you’ll also reflect on your own cultural experiences.

This course introduces digital archival collections that can be accessed and used as data for research and inquiry. Topics will focus on the transformation, analysis, and interpretation of digital cultural heritage in archival contexts, including digitization, web archiving, software emulation, and data archiving. From text messages, Spotify playlists, to the President's tweets--how are digital traces collected, preserved and managed by archives? What are the ethics of managing digital archives and making them accessible to researchers, the public, and machines?

Offered on the letter-grade basis only.

The class explores the principles of relational database design, and SQL as a query language in depth.

Principles and practices in Data Engineering. Emphasis on the data engineering lifecycle and how to build data pipelines to collect, transform, analyze and visualize data from operational systems. This is a hands-on and highly interactive course. Students will learn analytical data modeling techniques for organizing and querying data. They will learn how to transform data into dimensional models, how to build data products, and how to visualize the data. We will also examine the various roles data engineers can have in an organization and career paths for data professionals

This course will cover relevant fundamental concepts in machine learning (ML) and how they are used to solve real-world problems. Students will learn the theory behind a variety of machine learning tools and practice applying the tools to real-world data such as numerical data, textual data (natural language processing), and visual data (computer vision). Each class is divided into two segments: (a) Theory and Methods, a concise description of an ML concept, and (b) Lab Tutorial, a hands-on session on applying the theory just discussed to a real-world task on publicly available data. We will use Python for programming.   By the end of the course, the goals for the students are to: 1. Develop a sense of where to apply machine learning and where not to, and which ML algorithm to use 2. Understand the process of garnering and preprocessing a variety of “big” real-world data, to be used to train ML systems 3. Characterize the process to train machine learning algorithms and evaluate their performance 4. Develop programming skills to code in Python and use modern ML and scientific computing libraries like SciPy and scikit-learn 5. Propose a novel product/research-focused idea (this will be an iterative process), design and execute experiments, and present the findings and demos to a suitable audience (in this case, the class).

Practical skills and understandings required to effectively work with open source software and understand the projects that build them. Includes git-based collaboration as well as conceptual understanding of licenses, security, technical and social processes in open source development. Class projects involve working with digital trace data from open source repositories.

This course offers students in Information Science a comprehensive exploration into the theories, techniques, and tools of data visualization. It is designed to equip students with the skills to effectively communicate complex information visually, enabling data analysis and decision-making. Through a combination of lectures, hands-on projects, and case studies, students will learn how to design and implement effective and aesthetically appealing data visualizations for a variety of data types and audiences. Upon successful completion of this course, students will be able to: • Understand the principles and psychology of visual perception and how they influence data visualization. • Critically evaluate the effectiveness of different data visualization techniques for varying data types and user needs. • Master the use of leading data visualization tools and libraries such as D3.js, or Tableau. • Develop interactive dashboards and reports that effectively communicate findings to both technical and non-technical audiences. • Apply design principles to create visually appealing, accurate, and accessible data visualizations.

Introduction to the emerging field of Explainable Artificial Intelligence (XAI) from the perspectives of a developer and end-user. Students will gain hands-on experience with some of the most commonly used explainability techniques and algorithms.

Leveraging Text Mining, Natural Language Processing, and Computational Linguistics to address real-world textual data challenges, including document processing, keyword extraction, question answering, translation, summarization, sentiment analysis, search, recommendation, and information extraction. Each week, classes include (a) Theory and Methods for NLP concepts and (b) Lab Tutorials for practical application with Python on multilingual text datasets.

Provides students with an understanding of the fundamental concepts, common methods, and analytical tools of social network analysis. Students will gain experience applying both exploratory and qualitative methods to real-world problems within the social network domain.

Offered on the letter-grade basis only.

This class explores how to make arguments about and through design. The first half focuses on values, criticism, ethics, and analysis of technology, the latter portion aims to help a soon-to-graduate technologist envision positive social impact in a mission-driven enterprise. Students will practice synthesizing ethical tech considerations – as they will have to do for the rest of their careers – and combining this with an organizational mindset. Through exercises, role-playing, discussions, guest lectures from activist technologists, and wide-ranging readings, students will practice connecting broader implications of their designs with technical choices. Design for Social Impact seeks to arm students with diverse ways of reflecting on their authorial relationship to technology, drawing from art and design to political science and anthropology. Course participants will be encouraged to focus on areas of personal interest, enumerating the social, political, and economic parameters of particular technical systems: parameters that are as important as power consumption, usability, or efficiency.

Project-based learning course in which students will apply a combination of research and evaluation methods (scientific, sociological, historical, computational) to identify and explore a research question of community impact. Students will first learn about applied research and evaluation from a team of faculty and community-based nonprofit leaders, then work in small groups apply their knowledge.

Offered on the letter-grade basis only.

Overview of public health and the information systems used to achieve public health goals. This course is divided into three parts: (1) overview of public health, (2) fundamentals of public health informatics, and (3) public health information systems.

Leveraging medical claims data to guide population health interventions, primarily through the use of machine learning models. The course will focus on the data processing pipeline, and no prerequisite knowledge of machine learning models is required

New advanced topic in Health Infotmatics/Human-Centered Data Science planned for Fall 2024. Details not yet confirmed.

The course is designed for undergraduate students who are interested in understanding, analyzing, designing, evaluating, or developing technologies to serve the health needs of general consumers. It covers the concept of consumer health informatics, health behavior theories, health information seeking and information retrieval, various forms of consumer health systems, and the design and evaluation of such systems.

Offered on the letter-grade basis only.

This course examines disability beyond digital accessibility (i.e., web accessibility, user interface design) and focuses on disability from an organizational and socio-technical point of view. Students will learn about the legislation and policies impacting accessibility, the models that shape our perceptions of disability, and review case studies of disability in several contexts. In addition to the broader types of disabilities, we will consider other forms of disabilities (permanent, situational, temporary). Students will engage in class discussions, small group activities, homework assignments, and give oral presentations. Students will be equipped with the knowledge and skills to apply methods and models of accessibility in the workplace in various fields, including software design, data science, AI, and library science.

This class explores how to make arguments about and through design. The first half focuses on values, criticism, ethics, and analysis of technology, the latter portion aims to help a soon-to-graduate technologist envision positive social impact in a mission-driven enterprise. Students will practice synthesizing ethical tech considerations – as they will have to do for the rest of their careers – and combining this with an organizational mindset. Through exercises, role-playing, discussions, guest lectures from activist technologists, and wide-ranging readings, students will practice connecting broader implications of their designs with technical choices. Design for Social Impact seeks to arm students with diverse ways of reflecting on their authorial relationship to technology, drawing from art and design to political science and anthropology. Course participants will be encouraged to focus on areas of personal interest, enumerating the social, political, and economic parameters of particular technical systems: parameters that are as important as power consumption, usability, or efficiency.

Effective application of social and technical methods of analysis to specific existing systems with inseparable technical and social components to enable improvement. Covers techniques such as modeling, interviewing, observation, trace analysis, and benchmarking.

Offered on the letter-grade basis only.

In this course, we will work to understand and address the challenges of misinformation, disinformation, and strategic manipulation in online environments. First, we will work to develop a deep understanding of the problem space. We will read and discuss existing research (both historical and contemporary) on how and why misinformation and disinformation spread. Next, we will explore the process, both personal and interpersonal, by which these issues can be approached and addressed in our own lives. This will involve reflecting on our own presuppositions, beliefs, and biases about information; and doing a project in which we apply the principles of Human-Centered Design to investigate different design directions for addressing misleading information. Students will gain important contextual knowledge and hands-on design experience that they can take into future professional domains (from education to policy to technology), where they can contribute to building more trustworthy information systems.

Develop fundamental graphic design theory and skills to prepare students for careers in Informatics and related fields.

This course addresses concepts and methods of user experience (UX) research, from identifying users’ problems and needs to evaluating concepts and designs for viability, usability, and satisfaction. It also covers aspects of managing the research process, including recruiting participants, setting up and conducting studies, analyzing qualitative and quantitative data, and disseminating insights. Students will work both individually and as part of a team to complete research exercises and projects. The course includes hands-on practice with several common UX research methods such as observation, interview, survey, focus groups, and expert review. We will also touch on applied topics such as research in enterprises, consulting, and startup organizations, lean/agile techniques, mobile research approaches, and strategies for persuasively communicating findings and product implications.

This course focuses on the unique design practice of (1) representing and organizing information to facilitate perception and understanding (information architecture) and (2) specifying the appropriate mechanisms for accessing and manipulating task and play information (interaction design). This course also explores design patterns appropriate for the HCI professional.

Examine social and psychological experiences of virtual environments and immersive technologies, such as in virtual reality and augmented reality. Through the course students will learn about the immersive technology and the research behind people’s experiences of virtual environments.

Digital Accessibility has become a critical topic for product leaders, developers, UX designers, and usability researchers. This course will explore the legal, ethical, and practical aspects of Accessibility as it relates to creating inclusive products and experiences for persons with disabilities. While Accessibility applies to both the physical and digital world, a large portion of the course will be focused on digital experiences, and those that combine technology with devices and tools.

This course introduces human aspects of AI systems for UX design students. It will provide an overview of AI's psychological and societal implications and the opportunities to design AI-integrated products by applying human-centered design principles.

Introduction to the methodologies and techniques required for designing an ideal user experience with physical objects. Students will use qualitative, quantitative, and anthropometric data to design and iterate projects.

The first half of the course describes interaction design while the second half covers information design. Each student will keep a sketchbook and turn in sketches corresponding to exercises. No sketching experience is required. Each student will participate in a group project developing a prototype of an information artifact such as a website, app, or kiosk. The prototype is usually completed using Figma, which will be taught as part of the course. During the information design part of the course, students will be introduced to Tableau and have the opportunity to create a data visualization in Tableau.

In this course, we will work to understand and address the challenges of misinformation, disinformation, and strategic manipulation in online environments. First, we will work to develop a deep understanding of the problem space. We will read and discuss existing research (both historical and contemporary) on how and why misinformation and disinformation spread. Next, we will explore the process, both personal and interpersonal, by which these issues can be approached and addressed in our own lives. This will involve reflecting on our own presuppositions, beliefs, and biases about information; and doing a project in which we apply the principles of Human-Centered Design to investigate different design directions for addressing misleading information. Students will gain important contextual knowledge and hands-on design experience that they can take into future professional domains (from education to policy to technology), where they can contribute to building more trustworthy information systems.

In this course, we will work to understand and address the challenges of misinformation, disinformation, and strategic manipulation in online environments. First, we will work to develop a deep understanding of the problem space. We will read and discuss existing research (both historical and contemporary) on how and why misinformation and disinformation spread. Next, we will explore the process, both personal and interpersonal, by which these issues can be approached and addressed in our own lives. This will involve reflecting on our own presuppositions, beliefs, and biases about information; and doing a project in which we apply the principles of Human-Centered Design to investigate different design directions for addressing misleading information. Students will gain important contextual knowledge and hands-on design experience that they can take into future professional domains (from education to policy to technology), where they can contribute to building more trustworthy information systems.

This course is designed to help set students majoring in Informatics up for career success post-graduation. What does career success look like? Well, philosophically many things depending on context. This course, however, focuses on the transition between the last year of college and the first year of a career. The semester is broken into three units as a foundation to begin answering these questions: 1. The Landscape: What opportunities exist for graduates with my skillsets and interests? What do different job titles actually mean? How do I know which path is right for me? How do I find jobs and opportunities I’m interested in and qualified for? 2. The Application & Interview Process: After Unit 1, I know the kinds of positions and career paths I’m interested in post-graduation, but how I do actually get the job/position or accepted to my program of interest? In a sea of hundreds (sometimes thousands) of applicants, how do I show I’m a good fit in an application and an interview? Once I have options, how do I choose what’s right for me? 3. The First Year: How do I make sure my first year is successful? How do I navigate a new professional space? How do I set myself up to build relationships and perform well? How do I apply what I’ve learned in the Informatics Major and at UT to grow as an ethical, equitable leader and information professional? This course is held in-person and rather than exams, assignments are built as concrete materials students can use in their career searches and professional endeavors. Students will leave this course empowered to successfully navigate the Informatics-related job market and professional opportunities

Student works independently to accomplish an approved objective under the guidance of a member of the iSchool faculty, or an adjunct instructor approved by the Undergraduate Program Coordinator.

To enroll in this course, students must submit an approved Independent Study Proposal to the iSchool undergraduate student services office during an active registration period.

Student works independently to accomplish an approved objective under the guidance of a member of the iSchool faculty, or an adjunct instructor approved by the Undergraduate Program Coordinator.

To enroll in this course, students must submit an approved Independent Study Proposal to the iSchool undergraduate student services office during an active registration period.

Student works independently to accomplish an approved objective under the guidance of a member of the iSchool faculty, or an adjunct instructor approved by the Undergraduate Program Coordinator.

To enroll in this course, students must submit an approved Independent Study Proposal to the iSchool undergraduate student services office during an active registration period.

Student assists and collaborates with a member of the iSchool’s full time research faculty on a project directly supporting their academic research.

To enroll in this course, students must submit an approved Research Experience Proposal to the iSchool undergraduate student services office during an active registration period.

Student assists and collaborates with a member of the iSchool’s full time research faculty on a project directly supporting their academic research.

To enroll in this course, students must submit an approved Research Experience Proposal to the iSchool undergraduate student services office during an active registration period.

Student assists and collaborates with a member of the iSchool’s full time research faculty on a project directly supporting their academic research.

To enroll in this course, students must submit an approved Research Experience Proposal to the iSchool undergraduate student services office during an active registration period.

Offered on the credit/no credit basis only.

Offered on the credit/no credit basis only.

Offered on the credit/no credit basis only.

As the culminating experience of the undergraduate Informatics Program, I 379C allows every student to apply their unique skillsets and learnings to a "degree-capping" project that is focused on a real-world problem or initiative. Informatics Capstone projects can take many forms, but typically involve aligning on a specific project and plan with an industry or faculty project sponsor, and then completing the project over the course of the semester. This course is designed to support your capstone journey throughout the semester as you work on your project with your Field Supervisor. Progress in the course is measured through weekly updates and documents submitted directly to Canvas. During the semester, the course meets once per week, and during these sessions we'll focus on items and issues relevant to your capstone experience. You'll have an opportunity to present your work also, through class presentations and the final poster session where your sponsors, faculty, and other students can meet you and discuss your project. Summary of Course Goals 1. Deliver a professional-level project/solution to showcase your knowledge, skills, and abilities. 2. Take direction and feedback from a supervisor working in your applied field of study. 3. Strengthen communication and presentation skills. 4. Manage expectations around project goals, schedule, and deliverables.

Research, read, and develop an honors thesis subject and proposal for one semester; followed in the second semester by the writing and defense of a final honors thesis. Two-semester course taken as I 679HA (semester 1) and I 679HB (semester 2). Credit will be awarded upon completion of both semesters. This course has been approved by Undergraduate Studies to award the Independent Inquiry Flag and theWriting Flag. Interested students should speak with their advisor and submit an approved Informatics Honors Thesis Proposal prior to registration.

Research, read, and develop an honors thesis subject and proposal for one semester; followed in the second semester by the writing and defense of a final honors thesis. Two-semester course taken as I 679HA (semester 1) and I 679HB (semester 2). Credit will be awarded upon completion of both semesters. This course has been approved by Undergraduate Studies to award the Writing Flag. Interested students should speak with their advisor and submit an approved Informatics Honors Thesis Proposal prior to registration.

In this class we'll use history and readings to not only understand the current state of the information field, but how we got here. Seeing that, students will understand that they have the power to shape and improve the information field. Students will also work in in-class teams to cement ideas and connect to other students in the class. We work to answer the question of why UX designers, archivists, AI ethicists, and librarians are all in the same graduate program. Ultimately the goal is to connect, understand, and inspire.

The class focuses on developing problem solving skills using Python as a programming language. Starting from procedural function development, we also explore object-oriented techniques, and discuss simple data structures that are often used in software development. The students usually do a few programming assignments, take a midterm, and submit a final project.

In-depth study of a problem or topic related to information studies, usually culminating in an examination or a scholarly written report. Individual Instruction. With the consent of the graduate advisor, may be repeated for credit. INF 181 is worth 1 hour of semester credit. Students wanting 2 or 3 hours of credit should take INF 281 or INF 381 respectively. Individual Study registration/proposal form

In-depth study of a problem or topic related to information studies, usually culminating in an examination or a scholarly written report. Individual Instruction. With consent of the graduate advisor, may be repeated for credit. INF 281 is worth 2 hours of semester credit. Students wanting 1 or 3 hours of credit should take INF 181 or INF 381 respectively. Individual Study registration/proposal form

In-depth study of a problem or topic related to information studies, usually culminating in an examination or a scholarly written report. Individual Instruction. With consent of the graduate advisor, may be repeated for credit. INF 381 is worth 3 hours of semester credit. Students wanting 2 or 3 hours of credit should take INF 281 or INF 381 respectively. Individual Study registration/proposal form

What does it really mean to be user-centered? How do we practice user-centered design in a professional and methodical manner? What research findings can we rely on to help us improve user experiences? This is a readings/discussion course that examines in depth what we know about people (that is, what does scientific research actually tell us) and how can we apply this knowledge in the real-world of experience design. We examine human psychology, from physical ergonomics to cultural dispositions, stopping off on cognition and social analyses en route, so as to have a holistic, robust perspective on what it means to understand users. The readings are complemented with an examination of methods e.g., what is a cognitive walkthrough and how do you do it reliably? what are the limitations of heuristic evaluations? The goal is to give you a solid grounding in the practices of user-centered thinking, regardless of your area of application, and prepare you for professional level contributions in the user-experience world. There is no teamwork, all students deliver individual term papers and design critique diaries. There are also no pre-requisites -- technical or theoretical, the class is open to all.

Major reference resources and strategies useful in providing information services in libraries and other information agencies.

Examines the evaluation, selection, and use of books and other media for young adults of junior and senior high school age. Briefly surveys the reading experience, psychology of adolescence, and reading interests of young adults. Includes extensive reading and viewing. May be repeated for credit when the topics vary.

Explore the evaluation, selection, and use of books and other media and materials to meet the needs of children and young adults.

In this course, you will gain exposure to legal reference questions and the reference interview, investigate legal research sources, and beome familiar with the methods and strategies for conducting legal reference and basic legal research.

Evaluation and use of printed online information resources and services in specialized areas, with emphasis on new information technologies. Information-seeking behavior of users, document delivery, new roles of the information specialist in user support, and information needs of a variety of clients. May be repeated for credit when the topics vary.

Examine inquiry models and information-seeking theories relevant to K-12 teaching and learning. Explore tools and resources for student learning and strategies for teaching specific information literacy skills within the context of a research-based inquiry.

History and ongoing evolution of instruction in library and information service settings; conceptions of information literacy; learning theories and pedagogical approaches; instructional design principles, including backward design; and reflective teaching practice.

This course is a hands-on introduction to Digital Humanities, which may be defined as using digital technologies to investigate questions traditional to the humanities or to ask humanities-oriented questions about the digital. What are these questions? As usual, it depends, depends on the scholar’s theoretical orientation, methods, and resources at hand (including not only primary source materials, but time, skill, and support). This course is relevant to literary scholars, historians, media scholars, information scholars, and all those who are interested in how humanists engage in cultural studies. It will include learning to evaluate DH questions and DH projects through project-based exercises in creating and interpreting digital humanities resources and tools and a close (and critical) look at the infrastructural, institutional, and political issues involved in interrogating “the digital” in the humanities. As we look at the concepts, methods, theories, and resources of DH through the perspective of practice, we will consider how computational methods are being used to further humanities research but also, more importantly, how our understanding of computing technologies is deepened by humanities research. No prerequisites are required for this course.

Introduction to the concepts of information organization, representation, and classification. Consideration of different traditions of practice and user concerns.

Philosophical and social context, objectives, and methodology of evaluating, selecting, and managing library materials.

The science and engineering of building automated search engines: foundations and emerging methods, key models and approaches, front-end usability and back-end algorithms, theories of relevance, annotation practices, and system evaluation/benchmarking.

Principles and practices for describing information resources.

Introduction to the theoretical foundations, history, principles, and research surrounding the representation of information, digital collections, and data with metadata, with emphasis on concepts of standardization, infrastructure, formats, and exchange. Major topics will include metadata types, value and content standards, formats, data interchange standards and protocols. The course introduces participants to the examination and analysis surrounding issues of effectiveness, economics, values and audience surrounding different types of metadata applications. Provides background for further studies in information organization, preservation, and database management.

This course introduces students to human-computer interaction theories and design processes. The emphasis is on applied user experience (UX) design. However, the course starts by discussing fundamental aspects of human perception and cognition and linking them with design principles. The course presents an iterative evaluation-centered UX lifecycle and introduces students to a broader notion of user experience, including usability, usefulness, and emotional impact. The UX lifecycle should be viewed as template intended to be instantiated in many different ways to match the constraints of a particular development project. The UX lifecycle activities we cover include contextual inquiry and analysis, requirements extraction, design-informing models, design thinking, ideation, sketching, conceptual design, and formative evaluation.

This course explores the fundamental principles and practical applications of Information Architecture (IA). Drawing from the seminal work "Information Architecture: For the Web and Beyond" by Louis Rosenfeld, Peter Morville, and Jorge Arango, students will delve into the essential concepts, methodologies, and best practices shaping the organization and presentation of information in digital environments. Simply, this course addresses how to make content organized and findable based on human understanding. Throughout the course, students will examine the critical role of IA in enhancing user experience, facilitating navigation, and optimizing content discoverability. Topics covered include information organization, navigation design, metadata implementation, taxonomy development, and user-centered design principles. Through a combination of theoretical discussions, case studies, hands-on exercises, and a real project with a real client and real world constraints, students will gain proficiency in designing effective IA solutions tailored to diverse user needs and contexts. Emphasis will be placed on understanding user behavior, conducting user research, and iteratively refining IA structures to align with evolving user requirements and organizational goals. Course Objectives: Gain a comprehensive understanding of Information Architecture principles and methodologies. Learn how to analyze and evaluate existing IA structures in digital environments. Develop proficiency in designing and implementing effective IA solutions for websites and digital products. Explore techniques for conducting user research and applying user-centered design principles to IA. Understand the role of IA in enhancing usability, findability, and overall user experience. Acquire practical skills in wireframing, prototyping, and usability testing within an IA context. Explore emerging trends and technologies shaping the field of Information Architecture.

Database is the foundation of Data Science. It provides the unique design to store, retrieve, and manage data. Data become the essential gas to power the generative AI. How to model data, encode context, enforce business rules, and achieve efficiency are critical for database design. This course provides the introductory understanding of relational database design with the focus on three parts. The first part is centered around the database design lifecycle by introducing business rules, ER diagram, normalization, and UML chart. The second part talks about database query language SQL by explaining concepts and providing examples. The third part gives you the forward introduction of XML database which is the commonly used NoSQL database. The learning content will be delivered in the variety of exercises including lectures, tutorials, class activities, individual assignments, group assignments, and group projects. This course empathizes peer learning, hands-on practices, forward exploring, and risk taking.

The concept of consumer health informatics, health behavior theories, health information seeking and information retrieval, various forms of consumer health systems, and the design and evaluation of such systems

This course will give students a foundational introduction to user experience (also known as UX, CX, HCI) and introduce some of the core UX research methods in use today, as well as applying these methods to a product to create a final presentation that can hopefully be used in their portfolio/job seeking adventures. Accordingly, the class will cover 5 major areas: 1. Have an in-depth understanding of some primary UX methods relevant to product development (e.g. Heuristic evaluation, Moderated User testing, UX Benchmarking). 2. Understand the principles of other important UX tools/methods (e.g. Information architecture tests (card-sorts), RITE testing, Competitive Analysis, Thematic coding of qualitative data, etc.). 3. Have a working understanding of the most frequently used UX methods at each point of the development lifecycle, with a specific focus on which methods are best suited to evaluative research. 4. Learn the scientific underpinnings of the various methodologies, including the specific advantages and disadvantages of each. 5. The “real world” application of these skills to industry-paced projects

Introduction to the issues and trends in digital asset management and digitization initiatives, including planning and project management, asset delivery and management systems, interoperability and the importance of standards, copyright, metadata basics, digital preservation, and specific digitization processes for documents, images, video, and sound.

This course explores the life cycle of a digital library/collection through various critical lenses and hands-on experiences. The objective of the course is for students to obtain a solid understanding of the theoretical frameworks, technical processes, and technologies needed to build meaningful, ethical, and reusable digital libraries.

Repeatable with Different Topics

Exploration of major components of health IT systems, ranging from data semantics (ICD10), data interoperability (FHIR), diagnosis code (SNOMED CT), to workflow in clinical decision support systems. After establishing a good understanding of the fundamentals of health IT systems, we will dive deep into how AI innovations (e.g., machine learning, deep learning, computer vision) are transforming our healthcare system by introducing new concepts of mobile health, AI diagnosis, AI medicine, smart device, and intelligent delivery.

Evidence is information, and nearly all information is created, collected, communicated and stored electronically. Thus, the ability to identify, discover, interpret, authenticate and challenge electronically stored information is a crucial litigation skill. This course will seek to reconcile the federal rules and e-discovery case law with the sources, forms and methods of information technology and computer forensics. Students will explore information technology, learn to "speak geek" and acquire hands-on, practical training in finding electronic evidence, meeting preservation duties, guarding against spoliation, selecting forms of production, communicating and cooperating with opposing counsel and managing the vast volume and variety of digital evidence and metadata. With an emphasis on understanding the nuts and bolts of information technology, the course teaches practical considerations, tips and tools as well as pivotal case law that has shaped this area of the law and the electronic discovery industry as a whole.

This course will cover fundamental concepts in Machine Learning (ML). The course will provide conceptual and practical knowledge on a wide range of modern machine learning algorithms; including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), reinforcement learning & deep learning models (CNN, RNN, Autoencoders & Transformers) and also introduce the importance of Prompt Engineering and Retrieval Augmented Generation. The goal is for students to be comfortable and confident in machine learning concepts and have the ablity to build machine learning model solution to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, this is a great place to start.

Examination of propaganda and disinformation campaigns, the psychology behind how they work and how they became central to both the Cold War and political elections; as well as the advent of social media and algorithmic optimization to facilitate and accelerate the reach and impact.

The purpose of this course is to provide theoretical and practical foundations for information professionals who wish to design and evaluate search systems and services, taking user-centered approaches. This course explores search user interfaces, search behavior, search interaction, search user experience, search as learning, search for creativity, and research methods for understanding information behavior and evaluating search systems. Students will learn search behavior across various contexts, including academic and professional settings, everyday life, and digital learning environments. Students will gain insights into how people interact with, use, and evaluate information in a variety of application areas, such as web search engines, domain-specific search systems, digital libraries, social search platforms, and generative AI-based systems.

Accessible UX provides students working (or planning to work) in any area of UX, Digital Product Management, or Development with key skills and insights into the current accessibility landscape, in addition to specific guidelines and WCAG conformance specifications. The course is divided into foundational and tactical modules. The first half of the course provides a comprehensive overview of Accessibility and its importance. The second half of the course involves evaluating real-world applications and websites per the WCAG guidelines, producing Accessibility reports, planning studies (with persons with disabilities), and designing for accessibility. Course Goals 1. Become proficient in recognizing accessibility issues in key domains 2. Understand successful team and organizational behaviors in Accessibility 3. Learn how Accessible UX and Development is accomplished 4. Evaluate Web and App experiences using the WCAG framework from W3.org/WAI

Introduction to basic design concepts such as composition, color theory, interactions; the Lean UX methodology, history, predecessor, pros/cons, and adaptations on Lean UX and case studies from companies such as Google; application of rapid prototyping using the latest design tools and methods.

Data storytelling is more than sharing data—at its most simple, it’s about designing charts and tables that make sense to the people who will be using them and help those people make better, faster decisions. While making a chart is as easy as a few clicks, doing it well requires much more. There is a science to how our eyes and minds process information as well as an art to making good graphic design choices. This comes together in an effective data presentation when the work is readable, usable, and above all actionable—not just aesthetically pleasing (though we’ll certainly address that too). As information professionals, we are well-positioned to understand and design for the needs of our users, to interrogate our data sources thoughtfully, and to ask future-thinking questions. This course will also draw on elements from cognitive psychology, user experience, data journalism, graphic design, business, and more. This multidisciplinary approach will take us on a grand tour that will touch on many aspects of data analysis and will serve as an excellent introduction to other data-oriented courses in the iSchool master’s program.

n this course, we will work to understand and address the challenges of misinformation, disinformation, and strategic manipulation in online environments. First, we will work to develop a deep understanding of the problem space. We will read and discuss existing research (both historical and contemporary) on how and why misinformation and disinformation spread. Next, we will explore the process, both personal and interpersonal, by which these issues can be approached and addressed in our own lives. This will involve reflecting on our own presuppositions, beliefs, and biases about information; and doing a project in which we apply the principles of Human-Centered Design to investigate different design directions for addressing misleading information. Students will gain important contextual knowledge and hands-on design experience that they can take into future professional domains (from education to policy to technology), where they can contribute to building more trustworthy information systems.

The current Web has experienced tremendous changes to connect data, people, and knowledge. There are a couple of exciting efforts trying to bring the Web to its full potential. The Semantic Web is one of them which is heavily embedded in the Artificial Intelligence area with the long-term goal to enhance the human and machine interaction by representing data semantics, integrating data silos, and enabling intelligent search and discovery. This course aims to provide the basic overview of the Semantic Web in general, and data semantics in particular, and how they can be applied to enhance data integration and knowledge inference. Ontology is the backbone of the Semantic Web. It models the semantics of data and represents them in markup languages proposed by the World Wide Web Consortium (W3C). W3C plays a significant role in directing major efforts at specifying, developing, and deploying standards for sharing information. Semantically enriched data paves the crucial way to facilitate the Web functionality and interoperability. This course contains three parts: Semantic Web language, RDF graph database (i.e., RDF triple store), and its applications. The fundamental part of the course is the Semantic Web languages. It starts from XML and goes further to RDF and OWL. The RDF graph database part introduces different APIs of Jena and its reasoners. The application part showcases current trends on semantic applications. Prerequisites Basic knowledge of HTML and XML is desired. Course Objectives This course aims to develop a critical appreciation of semantic technologies as they are currently being developed. At the end of this course, students should be able to: • sketch the overall architecture of the Semantic Web. • identify the major technologies of the Semantic Web and explain their roles. • illustrate the design principles of the Semantic Web by applying the technologies. • understand certain limitations of the Semantic Web technologies, and be aware of the kinds of services it can and cannot deliver. Course aims are achieved through: • Lectures covers basic knowledge of the Semantic Web • Projects applying semantic technologies to concrete problems of information delivery and use • Assignments of practicing and utilizing key semantic technologies

Disaster events, like floods and fires, can cause severe damage and loss in cultural heritage collections. How will you respond when disaster strikes? This course introduces students to the fundamental preservation concepts, planning strategies, and applied response techniques that make a difference. The class will evaluate the physical impact of fire, water, soot, mold, and insects on varied media, including books, flat paper, audiovisual materials, and other items. Disaster planning exercises model proactive methods to minimize damage. Hands-on response drills enable students to practice salvage techniques, and to triage and prioritize impacted materials.

In this course, students will learn about the graphic designs role in UX design roles using industry-standard tools, Figma and Adobe Illustrator. This course is meant to engage and push students to think creatively to design and create portfolio-worthy polished designs. Students will learn to craft visually engaging and user-friendly digital experiences. By learning and understanding graphic design principles, including typography, color theory, and layout design, students will develop proficiency in designing and prototyping for a variety of experiences. These skills will be displayed through design exercises and projects.

Ethnographic research has found application and acceptance across various academic disciplines as well as industries. This course aims to introduce fundamental tenets of ethnographic methodology for investigating sociotechnical systems. Its foundation rests on interdisciplinary perspectives and anthropological insights, while simultaneously aligning with contemporary advancements such as design and speculative ethnography. The role of the future has perpetually held a central position in the utilization and shaping of technologies and information systems. A recurring narrative involves positioning a specific technology or system as "revolutionary" or "the future of" a certain domain. Adopting an ethnographic approach, this course seeks to critically examine sociotechnical imaginaries. Its objective is to glean insights from diverse communities, offering guidance in the construction of futures that are more inclusive, equitable, and diverse.

This course introduces the theory and practice of inclusive design principles for developing accessible technology. Inclusive design focuses on understanding the diversity of human characteristics (e.g., age, gender, race/ethnicity, disability, etc.) and applying a human-centered approach in designing technology to satisfy user requirements. Students will learn to use inclusive design processes to recognize user characteristics, discover user needs, produce design solutions, and develop prototypes during this course. Topics include, but are not limited to, inclusive design, ability-based design, disability-related terminology, and assistive technologies. Students will be required to engage in class discussions, complete in-class and homework assignments, give oral presentations, work in small groups, and complete a semester project. This course assumes students will have prior knowledge or experience in user experience (UX) design and/or human-computer interaction. No prior programming experience is required.

Academic and research oriented introduction to computational methods from a social science research design perspective.

As an industry practitioner with over a dozen years of product management experience and a dozen years of experience as a UX professional, I'm really looking forward to teaching a course that melds these best of both worlds. This course will focus on the fundamentals of product management and the tools and techniques employed by product managers. Students will learn about the corporate product phase gates and all the cross-functional teams with whom product managers engage. The product journey will be examined exploring: 1) how successful products are conceived; 2) how they intercept and are matched with appropriate technologies at the right time; 3) how their markets are analyzed; 4) how their volumes, revenues and profits are forecast yielding their business cases; and 5) how their value propositions are communicated to corporate executives to be formally approved and added to a company’s product roadmap. Furthermore, software and hardware product development processes will be investigated with an emphasis on how UX professionals can help streamline these processes and deliver user experiences that delight customers. This will have the effect of strengthening the product’s business case and ultimately the product’s return on investment (ROI), providing an evangelization opportunity for the product, the company, the product manager, and the UX professional.

Infrastructure is all around us, even (or perhaps especially) where we do not actively consider or account for it. In this course, students will learn how knowledge infrastructures such as repositories, classification systems, databases, networks, standards, and/or metadata both shape and are shaped by governmental policy, institutional decision making, technical advances, and professional and personal value systems. We consider how infrastructure matters in professional, personal, and political life, and employ infrastructure as a lens to evaluate and understand the legal, ethical, and policy consequences of knowledge work, data science, and information management. In this course, students will employ an infrastructural perspective to evaluate programs, systems, policies, and/or organizations. We will explore the consequences and societal impact of knowledge work at both global and local scales, and consider how infrastructure might be built or refined to support societal or organizational goals such as social justice, privacy, innovation, health, or security. This is primarily a discussion-oriented course, with assessment primarily coming through a multi-stage, semester-long, project oriented around a program evaluation.

This class explores various data science models, both traditional and the state of the art techniques. The course is designed to provide mathematical and computational basis such as Linear Algebra, Optimization techniques, and probabilistic modeling for different types of machine learning models. The goal of the class is provide a foundational basis for data science techniques. The class focuses on PSETs and a final data science project.

The emphasis of this course will focus on how to be successful in a UX career in a corporate setting, focusing on hardware product design. Students will become familiar with product development phase gates leading up to a product's launch, and what UX methodologies might be most useful to employ at each phase. In order to build cross-functional empathy, we will explore the roles of the extended product team players (e.g. Architecture, Marketing, Engineering, Program Management, Product Management, Industrial Design, Interaction Design, etc.) and discuss when to engage with them and how. We will also explore how to align design recommendations with a company's financial goals by touching on how to read a company's quarterly report, prioritizing UX requirements through a fiscally responsible lens, and business case development. My goal for this course is that students come away with skills that will equip them with awareness of each corporate function's lingo ("talk the talk") and modus operandi ("walk the walk"), such that they can successfully ensure the adoption of their UX designs and recommendations. Implementing what we learn will also forge strong rapport and relationships with other product team counterparts. This rapport will pay off future dividends and help differentiate ourselves from others in our discipline, building a strong foundation from which to launch a fruitful UX career.

This course examines key issues, challenges, and opportunities in the creation, management, and leadership of nonprofit organizations. Among the topics to be considered are nonprofit creation, mission management, organizational leadership, funding strategies, collaboration, and the impact of the public policy environment. We will use exercises and case studies to apply broad themes to practical issues. Guest speakers will periodically share their experiences running nonprofits. Relevant to students planning to work in, with, or through nonprofit organizations in a variety of fields (e.g., arts and culture, human services etc.).

Introduction to structures and practices within the rapidly-evolving field of librarianship. prepares students for their graduate work here.

In this class, we will explore different strategies for including games in collections across libraries, archives, and museums using case studies of specific institutions. We will also address unique qualities of digital and board games that make them challenging to existing practice in the field. Students will have hands-on experience with games through the semester.

Foundations of evaluation, including using the logic model, program evaluation, data gathering, analysis and reporting. Examples of evaluation and assessment in public, academic, and special are explored, with an emphasis on how libraries use data and evaluation to inform decision-making and support advocacy.

Students will learn to produce prototypes of information artifacts such as websites or apps, usually using Figma. The prototypes will be completed in groups. Students will also keep a sketchbook throughout the semester and will complete sketching exercises. No previous sketching experience is required. Students will learn the difference between lofi and hifi prototypes and complete examples of both. Lectures will describe prototyping in different forms and will also describe activities that support prototyping.

Computer vision is a field of artificial intelligence (AI) that enables computing systems to extract meaningful information from digital images, videos, and other visual inputs to make computable decisions. The camera captures digital photos and videos, and algorithms process and derive valuable information, which can help in making better decisions and recommendations. This course offers a comprehensive review of computer vision emphasizing its fundamental principles and their applicability in the real world, from fashion to deep face recognition.

Processes, techniques, and technologies that generate inscriptions (ready-to-take data), especially from or about people(s) or culture(s). Contexts, consequences, and history of datafication practices. Purposive intervention with datafication processes, practices, and artifacts.

Recently Deep Learning (DL) techniques have shown a lot of promise for tasks in various modalities such as speech, language, and vision and DL has become a go-to machine learning paradigm for Artificial Intelligence (AI) based applications. The course aims to cover theoretical and applied aspects of Deep Learning and how it is used to solve real-world problems. Classes in each week may be divided into two segments: (a) Theory and Methods, a concise description of a deep learning algorithm, and (b) Lab Tutorial, a hands-on session on applying the algorithm on multimodal real world data such as textual, visual and audio data.

Natural Language Processing (NLP) is concerned with interactions between computers and humans through the medium of human languages. It involves analyzing, understanding, and generating human language, making it possible for machines to interpret and respond to human speech and text. NLP is currently making significant contributions to modern technological advancements and serves as the backbone of crucial applications such as Gen AI, Conversational AI, Question Answering, Human Language Translation, Summarization, Sentiment and Emotion Analysis, Search and Recommendation, and Information Extraction in various domains such as healthcare, finance, legal, libraries and education and beyond. The proposed graduate-level course aims to cover fundamental concepts in Natural Language Processing / Computational Linguistics and how they are used to solve real-world problems. Classes in each week will be divided into two segments: (a) Theory and Methods, a concise description of an NLP concept, and (b) Practicum, a hands-on session on applying the theory to a real-world task on publicly available multilingual text datasets. We will use Python for programming along with popular libraries for text processing such as NLTK, SpaCy and HuggingFace's transformers. By the end of the course, the goals for the students are to: 1. Understand the process of garnering and pre-processing a large amount of multilingual textual data from various domains and sources. Characterize the processes to store, load, pre-process multilingual data and apply language processing operations such as normalization, tokenization, lemmatization, chunking and machine readable representation (vector) extraction. 2. Train machine learning algorithms for natural language understanding and generation and evaluate their performance. 3. Learn to extract information from unstructured text and represent them in the form of knowledge graphs 4. Learn to use existing knowledge graphs, ontologies and lexical knowledge networks for predictive analysis on text 5. Learn about popular NLP applications and tasks and the process of building such applications 6. Propose a novel product/research-focused idea (this will be an iterative process), design and execute experiments, and present the findings and demos to a suitable audience (in this case, the class).

A practical introduction and guide for using statistics to solve quantitative problems in user research. Many designers and user researchers view usability and user research as qualitative activities, which do not use formulas and numbers. However, usability practitioners and user researchers are increasingly expected to quantify the benefits of their efforts. The impact of good and bad designs can be quantified in terms of user performance, task completion rates and times, perceived user satisfaction. The course will address questions frequently faced by user researchers, such as, how to compare usability of products for A/B testing and competitive analysis, how to measure the interaction behavior and attitudes of users, how to estimate the number of users needed for usability testing. The course will introduce students to a foundation for statistical theories and the best practices needed to apply them. It will cover descriptive statistics, confidence intervals, standardized usability questionnaires, correlation, regression, and analysis of variance. It will also address how to effectively communicate the quantitative results.

This course is tailored for graduate students in Information Science who wish to deepen their understanding and skills in JavaScript, a cornerstone technology of the web. The course bridges theoretical concepts with practical application, preparing students to tackle complex problems in data processing, visualization, and web development within the realm of Information Science. The course incorporates multiple ways of learning including: readings, lectures, class discussion, in-class pair programming exercises, and project-base learning. Upon completion of this course, students will: • Gain an in-depth understanding of JavaScript's core concepts, including ES6+ features, asynchronous programming, and the event loop. • Learn to efficiently manipulate and process data using JavaScript, leveraging its interaction with APIs, and other web technologies. • Develop skills in creating interactive and dynamic data visualizations using packages such as D3.js or and other JavaScript-based visualization tools. • Understand the principles of web security, performance optimization, and best practices in JavaScript coding standards.

Explore hypothesis generation, design and set up of experiments (AB tests), quasi-experimental methods (regression, matching, heterogeneous treatment effects, etc.), interpreting results and uncertainty, and communicating insights to various audiences; with a focus on impacting practical business and product decisions.

Explore common data collection, management, and sharing practices in information technology and emerging technologies. Students will examine the human, social, and ethical impact of these practices and work on group projects to design data systems that are centered around broader impact and social responsibilities.

Visual, numerical, textual, and verbal presentation of information based on fundamental theories of human information perception and communication. Examples may include tables, graphs, dashboards, infographics, and reports.

Examine the opportunities and challenges for using crowdsourcing to teach computers to "see".

Introduction to the theory, methods, and applications of human computation and crowdsourcing; covering a breadth of key concepts as well as more specialized depth in one or more key sub-areas.

Learning key data wrangling maneuvers in abstract and implementations in SQL, Excel, R Tidyverse, and Python Pandas. Maneuvers in data transformations include Nest, Pivot, Mutate (inc. separate/unite), Group/Summarize and Rectangling. Projects include working with "wild caught" data datasets (usually CSV or JSON) and computational notebook environments (e.g., iPython, Jupyter, Rmarkdown, Quarto). Fall 2024 has changes from previous syllabus now that we have Database Design and Introduction to Programming. Nonetheless, the previous syllabus is still useful as it links to course materials that show the teaching approach and type of assignments. http://howisonlab.github.io/datawrangling/#Schedule_of_classes

This team-oriented project course will explore several issues surrounding the design and production of usable and elegant interactive experiences. Students will be introduced to topics including the iterative design process, physical and digital prototyping, and user testing. Project work will allow students to demonstrate mastery of the methods discussed in class through the creation and evaluation of screen-based and physical interfaces. Nor formal programming experience is necessary or expected as students are encouraged to leverage existing skills to develop visualizations and prototypes. For projects in the digital domain, experience with Figma, HTML5, Axure, Invision or the like is helpful, but not required.

Artificial intelligence (AI) is both a product of and a major influence on society. As AI plays an increasingly important role in society, it is critical to understand both the ethical factors that influence the design of AI and the ethical dimensions of the impacts of AI in society. The goal of this course is to prepare students for the important ethical responsibilities that come with developing systems that may have consequential, even life-and-death, consequences. Students first learn about both the history of ethics and the history of AI, to understand the basis for contemporary, global ethical perspectives (including non-Western and feminist perspectives) and the factors that have influenced the design, development, and deployment of AI-based systems. Students then explore the societal dimensions of the ethics and values of AI. Finally, students explore the technical dimensions of the ethics and values of AI, including design considerations such as fairness, accountability, transparency, power, and agency. Students who perform well in this class will be positioned to take on a leadership role within their organizations and will be able to help guide and steer the design, development, and deployment of AI-based systems in ways that benefit users, other stakeholders, their organizations, and society. The knowledge and skill gained through this course will benefit students throughout their careers, and society as a whole will benefit from ensuring that studenrs are prepared to consider the important ethical dimensions of their work.

Introduction to combining human and machine intelligence to benefit people and society. Explore cutting-edge research on a number of subjects related to human-AI interaction, including the psychological and societal impacts of AI as well as design guidelines and methods for human-centered AI.

Introduction to health informatics; includes fundamentals of information in biomedicine, nursing, public health, bioinformatics and genomics, electronic records, and integrated systems.

Examines information as a cultural phenomenon; may include e-commerce, privacy and secrecy, censorship, information as a commodity, Internet culture, access to cultural heritage, and control of the cultural record. Repeatable with Different Topics

Exhibits are a powerful way for libraries, archives, museums, and cultural institutions to engage the public with their collections. This course offers students the opportunity to plan and install an exhibit, focusing on objectives such as: crafting a narrative around physical objects; drafting exhibit text; accommodating media preservation issues; building basic display supports; and publicizing the exhibit. Students will learn about the historical origins of modern-day exhibit practices and will visit and evaluate current exhibits on campus and in the Austin area.

Theory and practice in the design, behavior, evaluation, and administration of libraries and other information agencies and systems. Marketing of information organizations and resources. Administrative applications of technology. May be repeated for credit when the topics vary.

Designed for students pursuing Texas Education Agency (TEA) certification in school librarianship. Examine the philosophy, objectives, and management of the school library with an emphasis on standards and competencies, and the roles of the school librari

Designed for students pursuing Texas Education Agency (TEA) certification in school librarianship. Examine the philosophy, objectives, standards, and management of the school library with an emphasis on the roles of the school librarian as an instructiona

This course will develop your skills to effectively manage a library, or information organization. We’ll be looking at problems faced by many types of libraries: public, academic, school, special. We’ll examine staffing, budget, collection development, patron behavior, and managing the expectations of users.

The process of exhibit creation in historical museums, from planning through development to opening and maintenance, as a negotiation among stakeholders for influence on the story that is told. Students visit local historical museums and examine how presentations are influenced by the institutional position of the museum, including its history and resources; the concerns of museum employees; the influence of the audience and of those who are directly affected or represented by an exhibit; and the role of contractual professionals.

As the culminating experience of the MSIS program, INF 388L allows every student to apply their unique skillsets and learnings to a professional project that is focused on a real-world problem or initiative. The course is designed to support your capstone journey throughout the semester as you work on your project with your project Field Supervisor. As an asynchronous course, students and instructors communicate via Canvas and various discussion prompts. Progress in the course is measured through updates and documents submitted directly to Canvas. During the semester, time is allotted for 1-on-1 meetings between student and instructor, and for small group meetings, as needed. Summary of Course Goals 1. Deliver a professional-level project/solution to showcase your knowledge, skills, and abilities. 2. Take direction and feedback from a supervisor working in your applied field of study. 3. Strengthen communication and presentation skills. 4. Manage expectations around project goals, schedule, and deliverables.

Designed for students pursuing Texas Education Agency (TEA) certification in school librarianship. 160 hours of field work in varied school library settings under the supervision of a qualified field supervisor and site supervisor.

Minimum 40 hours of supervised fieldwork for one semester. For each semester hour of credit earned, the equivalent of one lecture hour a week for one semester, with additional hours to be arranged. Offered on the credit/no credit basis only. May not be counted toward any degree in the School of Information. Required Form: https://www.ischool.utexas.edu/sites/default/files/images/iSchool_x88T_Application_Form.pdf

Minimum 80 hours of supervised fieldwork for one semester. For each semester hour of credit earned, the equivalent of one lecture hour a week for one semester, with additional hours to be arranged. Offered on the credit/no credit basis only. May not be counted toward any degree in the School of Information. Required Form: https://www.ischool.utexas.edu/sites/default/files/images/iSchool_x88T_Application_Form.pdf

Supervised fieldwork. Minimum 125 hours of supervised fieldwork for one semester. Offered on the credit/no credit basis only. May not be counted toward any degree in the School of Information. Required Form: https://www.ischool.utexas.edu/sites/default/files/images/iSchool_x88T_Application_Form.pdf

Systems for controlling recorded information in an organizational setting.

Examines personal recordkeeping and information management to explore the creation, management, and preservation of digital information. Includes current developments in digital technology that affect recordkeeping.

Introduction to the records aspect of archival enterprise, from acquisition to use, with emphasis on arrangement and description.

This course will introduce students to contemporary issues in archival studies through readings, research, writing, group discussion, and visits from leaders in the archival studies field.

Critical examination of conflicts and trends in information policy in private organizations and in federal, state, and international public-sector organizations. Repeatable with Different Topics

This course examines the U.S. communication policy in light of domestic and international structural, economic and technological changes. We will investigate how notions of control, access and expression have changed during the 20th and the 21st centuries, examining communication policies and regulation against a backdrop of technological innovation. The definitions and controversies around what constitutes the public interest intersect with policies for specific media systems including broadcasting, cablecasting, the Internet and social media, among others. The cultural ramifications of communication systems in terms of their impacts on people and on speech are a related domain we will address. At the current moment, issues around privacy, large tech companies and their role in contemporary life, the limits and authority of regulation, and of course social media,AI and ‘big data’ dominate many political and research agendas. Our goal will be to understand the backgrounds and foundations that bring us to these concerns and to frame them in critical ways.

A deep dive into a broad range of legal and policy issues associated with cybersecurity, intended as a comprehensive introduction to the topic and the many public and private institutions involved in it. See instructor's previous syllabus sample.

Topics in the theoretical, methodological, and practical aspects of information studies. Repeatable with Different Topics.

The individual student works under supervision of a member of the graduate faculty. Students must present the faculty member's name to the iSchool Registration/Course Administrator for registration.

The individual student works under supervision of a member of the graduate faculty. Students must present the faculty member's name to the iSchool Registration/Course Administrator for registration.

This course provides an overview of the major subject areas, ideas, concepts, and theories of Information Studies and introduces the basics of research, publication, and academic conventions in Information Studies. Prerequisites: Admission to the doctoral program and consent of the graduate advisor.

An overview of the nature and purposes of research, and common methods and methodologies in information studies.

An overview of concepts, results, and perspectives from philosophical, social science, humanistic, design, and technological disciplines that provide important underpinnings for information studies.

Explores a variety of approaches to qualitative methods including ethnography, participant observation, case studies, grounded theory, phenomenology, action research, and so forth. Students will have a hands-on opportunity to conduct their own research project in which they will learn, discuss, and reflect upon the procedures of qualitative research.

This course starts by discussing broad landscape of epistemological and theoretical perspectives and styles of reasoning and by situating in it quantitative research. It introduces you to the foundational concepts in quantitative research methods, such as causality, conceptualization, operationalization, measurement and sampling. It presents experimental design, survey design, and basic descriptive and inferential (frequentist) statistics, as well as a brief introduction to Bayesian inference and statistics.

Intensive writing, critique, and rewriting to assist senior doctoral students with refining their research writing in preparation for qualifying papers, dissertation proposals, and formal publications. May be repeated for credit.

Management of specific preservation strategies for the cultural record, with an emphasis on assessing preservation needs of a collection and grant writing.

Study of audio recording through a chronological examination of the development of recording; basic care and preservation of recordings; Preservation of audio archives; and stability concerns of audiovisual media.

Analysis, housing, and treatment of physical objects. Repeatable with Different Topics

Introduction to the ethical principles of conservation; conservation documentation; and hands-on treatment techniques for prolonging the lifetime of cultural materials.

Ever wondered how libraries and archives safeguard historical materials for future generations? Preservation is the answer. In this course, students learn collections care strategies that enable today’s information stewards to protect our growing cultural record. Scientific foundations and practical exercises will address common preservation challenges, such as environmental control, mold, insects, pollutants, and light damage. Modern topics in health, safety, and sustainability will highlight the developing nature of the field. Students will evaluate preservation risks for books, paper, electronic media, and other collections materials.

Basic techniques for care and handling of bound materials including but not limited to sewing structure, minor mends, and enclosures.

Methods and subjects of research in information studies. Repeatable with Different Topics NOTE: MSIS students must earn a grade of B or better in the MSIS core courses (below) in order for the courses to apply to the master's degree. A grade of B- does NOT satisfy this requirement.

This class equips thoughtful thinkers with powerful data science skills. You will learn how to manage and work with complex and big datasets in social science research, particularly in policy and nonprofit studies. You are expected to learn the following skills and respond to "big questions" that have social importance: 1) Understand the structure of data and how to work with big and complex datasets; 2) Understand the workflows of acquiring and managing data; 3) Able to conduct data-intensive and replicable social science research. *NOTE: In previous years, this topic was offered as INF 385T. These classes are identical, and students may not receive credit for both versions.

Large datasets are increasingly becoming available across many sectors such as healthcare, energy, and online markets. This course focuses on methods that allow “learning” from such datasets to uncover underlying relationships and patterns in the data, with a focus on predictive performance of various models that can be built to represent the underlying function generating the data. Topics to be covered: Linear Regression, Classification, Resampling Methods, Linear Model Selection and Regularization, Tree-Based Methods, Support Vector Machines, Unsupervised Learning (Clustering).

The individual student works under supervision of a member of the graduate faculty. Students must present the faculty member's name to the graduate coordinator for registration. Offered on the credit/no credit basis only. Please refer to the iSchool Capstone Handbook for instructions on how to register for the Master's Thesis. NOTE: All Capstone courses require student participation in an end-of-semester poster session. Please see poster session guidelines at http://www.ischool.utexas.edu/programs/masters/capstone/poster_session_guidelines for further information.

The individual student works under supervision of a member of the graduate faculty. Students must present the faculty member's name to the graduate coordinator for registration. INF 698B can only be taken immediately after completion of INF 698A. To register, please contact the iSchool Course Administrator. Offered on the credit/no credit basis only. NOTE: Besides working with their individual Faculty Supervisors, students must also contact the instructors of INF 388L/R in order to attend aggregated Capstone class meetings during the times currently scheduled for INF 388L/R. All Capstone courses require student participation in an end-of-semester poster session.

Preparation of a report to fulfill the requirement for the master's degree under the report option. Offered on the credit/no credit basis only. The individual student works under supervision of a member of the graduate faculty. Students must present the faculty member's name to the graduate coordinator for registration. Please refer to the iSchool Capstone Handbook for instructions on how to register for the Master's Report. NOTE: Besides working with their individual Faculty Supervisors, students must also contact the instructors of INF 388L/R in order to attend aggregated Capstone class meetings during the times currently scheduled for INF 388L/R. All Capstone courses require student participation in an end-of-semester poster session.

Teaching strategies for course design, syllabus creation, material development, classroom activities, student engagement, and grading. Additional subjects may include negotiation of course load and timing, course marketing, TA management, online teaching, and doctoral teaching/advising. May be repeated for credit as a teaching practicum.

Writing of the dissertation. The individual student works under supervision of a member of the graduate faculty. Students must present the faculty member's name to the graduate coordinator for registration. May be repeated for credit. Offered on the credit/no credit basis only.

Writing of the dissertation. The individual student works under supervision of a member of the graduate faculty. Students must present the faculty member's name to the graduate coordinator for registration. May be repeated for credit. Offered on the credit/no credit basis only.

Writing of the dissertation. The individual student works under supervision of a member of the graduate faculty. Students must present the faculty member's name to the graduate coordinator for registration. May be repeated for credit. Offered on the credit/no credit basis only.