Empirical Methods in Human-Centered Computing

Empirical Methods in Human-Centered Computing

Empiricism is a branch of epistemology—how we come to know something—that emphasizes the role of data or empirical evidence in the formation of new knowledge or ideas. In contrast to rationalism and skepticism, empiricism argues that we know something by means of our senses (sensory experience), particularly by observation and experimentation.

Empiricism has always been at the heart of the scientific paradigm. This scientific paradigm dates back to the sixteenth century, to Francis Bacon, who is often called the father of empiricism. Because for centuries science has been synonymous with the process of forming hypotheses, testing them through experiments, and using successful hypotheses to form models that explain and predict world phenomena, there used to be a debate whether computer science is science. That debate is long forgone. Only to make an interesting appearance in some otherwise pedestrian academic discourse in a History of Science class. However, it is still significant to recall that “Computing science follows this [scientific] paradigm in studying information processes.” (Denning, 2005). But how to study information processes, when humans are involved? When human information processes interact with automated processes?

empirical methodsIf a research project involves building human-centered computing (HCC) applications or understanding human-computer interactions, then to assess project success human-subject studies are a must. More and more, we are building computing tools that directly interface with humans—to improve student learning, benefit group work, create a healthy habit, provide better recommendations, crowdsource our next trip, protect our sensitive data from malicious websites, or gather critical insights from a big data visualization. How do we know that these computing systems are effective? What evidence do we have that the research project was successful? How do we conduct a meaningful assessment? What are the appropriate metrics? How can we improve the system? To answer all these questions, an in-depth understanding of the state-of-the-art empirical methods in HCC is essential.

This Fall, I will be offering a Special Topics class on Empirical Methods—focusing on an array of qualitative and quantitative methods.

In this course, students will become broadly familiarized with the state of the art empirical methods in human-centered computing. Students will learn how to use human subject data to (1) systematically evaluate human-computer interactions and (2) provide insights to inform the design of human-centered computing systems.

Upon successful completion of this course, students will be able to (somewhat independently) design and (reasonably) conduct human subject experiments in any area of computing. For more details, see here.

header photo credits: elsevier

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