Undergraduate and Master’s level
Evaluating Human-Centered Technology (MI 350): In this course, students will learn how to design and conduct studies that evaluate technologies based on user needs and goals, and measure fundamental usability problems. Students will be exposed to numerous summative evaluation methods, and will conduct both lab-based and “in the wild” summative evaluations. Students will learn how to pick up new summative evaluation methods, compare method pros and cons, and determine when each method is appropriate. Students will also learn how to summarize their evaluation results into reports and design suggestions. The goal of this course is to prepare students for careers in user experience design and evaluation positions.
Reasoning With Data (MI 320): In this course, students will learn how to explore and summarize structured data using simple statistics such as means, standard deviations, contingency tables, t-tests, correlations and regression. Students will also learn the basics of creating graphs to explore datasets and identify patterns and relationships. The goal of this course is to prepare students to use data to evaluate arguments and draw conclusions about technology performance and human behavior. After completing this course, students will be able to: Write scripts in R to load and manipulate data; Do exploratory data analysis, consisting of creating graphs and calculating descriptive statistics that summarize patterns in the data; and Create reports that incorporate code, output, graphs and text (prose) explaining the important patterns in the data.
Digital Footprints (MI 239): Each of us intentionally and unintentionally leaves behind digital information traces, our “digital footprint”, as we go about our daily lives. This undergraduate course provides a high-level overview of the different kinds of technologies involved in capturing this information, who owns it and controls it, and how it is used to both make our lives easier and at the same time more publicly visible. The goal is to increase awareness of what others can learn about us through these digital traces, to think about what we might be gaining (and losing) as individuals and society by allowing our digital footprints to continue to expand, and to debate what future technologies and policies concerning this information should be like.
Fundamentals of HCI (MI 845): This Master’s-level course introduces graduate students to the interdisciplinary field of Human Computer Interaction (HCI). We will be exploring wide-ranging themes and principles behind 30+ years of research and practice in this applied field, concerned with such topics as networked environments, social and mobile computing, virtual environments and information appliances, and traditional media. The course is designed around the Erickson & McDonald book (2008), HCI Remixed: Reflections on Works that have Influenced the HCI Community, which contains essays written by experts in the field of HCI about published works they found to be influential or inspirational. As such, the book provides an interesting framework from which to critique and explore themes, principles, and trends in the field of HCI over time. Students training to be HCI practitioners benefit from learning about how similar issues have been approached and solved in different ways as technologies have evolved over time, as well as becoming more aware of where to learn more when faced with a new design problem or situation. Students more interested in conducting research related to HCI benefit from exposure to a wide range of ideas and contributions in the field, that can serve as a solid foundation for their future work.
Doctoral level special topics courses
Methods for Computational Communication: Many computational communication research projects include the need to develop custom research software and use databases (e.g., social media data collection, log data collection and analysis, social network analysis, text analysis, experiments requiring custom software, etc.). This course will address topics such as, what kinds of research questions can methods that involve developing software help to answer? How is developing an experiment system different from writing a data scraper or scripting a complex survey in Qualtrics? What do reasonable requirements for research software development look like? How can one determine whether it is feasible to build the software oneself vs. hiring a student or professional? How should one include money for software developers in research proposals? How should one test custom software to ensure it works properly? This course will help students to learn a foundation that will enable them to pursue further topics and develop skills they will need to become successful as PIs at organizing, supervising, and managing research projects that require some amount of software development. No programming or software development background is necessary.
Human Computer Interaction: This course is a survey of current topics, theory and methods in HCI. The goal of this course is to provide an overview of what research in HCI looks like, including the kinds of research and design questions HCI researchers ask, the problems they want to solve, and the methods and theories they use. HCI is an interdisciplinary / multidisciplinary field that involves both research and practice. Students who are interested in some combination of human behavior, design, and technology / computation will be most interested and engaged by the course, regardless of their “home” discipline. This course is designed around the Olson & Kellog book, Ways of Knowing in HCI, which contains essays written by experts in the field of HCI about “ways of knowing” they use to investigate HCI research questions and problems. The field of HCI is quite broad; the focus of the book is to help HCI researchers become more knowledgeable about this diversity, to become more educated readers and reviewers, and also to be better able to choose the right approach for their own work. Students who are interested in conducting research related to HCI will benefit from exposure to a wide range of ideas and contributions in the field that can serve as a solid foundation for their future work.
Current Topics in Social Media Research: In this PhD-level seminar course we read and critique a selection of very recently published research papers that use different ideological and methodological approaches to understand and explain behavior and usage patterns in large-scale socio-technical systems (also known as “social media” or “social computing” systems). Social media researchers come from a variety of disciplinary backgrounds; therefore, we examine research conducted in the traditions of psychology, economics, communication, library science, sociology, and computer science, among others. Class discussions focus on both the results of the research, and comparisons of the different approaches. Course work includes a literature and research project proposal.