Think Like A Ux Researcher Pdf Download [PATCHED]
Laveta Nachman
Think like a Researcher is designed to help undergraduate students become comfortable with the research skills and tools needed to participate. It is designed to bring students, especially first year and second year students, from different colleges together to learn. Topics include benefits and challenges of research, finding faculty mentors, videos with advice from student researchers, examples of student projects and more. Learn about the wide variety of ways to get started with research including volunteer, getting a job, doing research for course credit, the Undergraduate Research Opportunities Program (UROP), Undergraduate Research Scholarship (URS), summer research and more. Learn more about the Office for Undergraduate Research and the University Libraries.
Teaching Research and Information Literacy (TRAIL) is a collaboration between the Merritt Writing Program (MWP) and the Library to integrate activities, readings, and reflections about the research process into writing curriculum. The goal of this collaboration is to help students develop the knowledge, skills, and attitudes needed to think like researchers.
This process involved MWP faculty defining the writing assignments they would include. Then librarians proceeded to build lessons, develop activities, identify readings, and create tutorials that would introduce students to the research process and increase their information literacy skills. This content focused on topics related to the research process such as avoiding researcher bias, understanding the information cycle, and developing a research question. Writing faculty used these materials in spring 2014 as four Writing faculty (Matt Moberly, Grace Rocha, Heather Devrick, and Tanvi Patel) piloted this TRAIL curriculum in six sections.
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Once students learn a clear strategy to develop questions, they must become researchers able to find answers. In most classrooms, the closest students get to research is using the Google Search engine, but that is only one strategy for gathering information. As a doctoral student in the middle of what feels like endless research, I believe it is valuable to teach students to extend their data collection beyond an online search.
If students learn how to generate questions and conduct research to answer those questions, they are more likely to take that researcher mindset into the world and continue learning long after they have left our classrooms.
The findings suggest modern computers may not be as different from humans as we think, and demonstrate how advances in artificial intelligence continue to narrow the gap between the visual abilities of people and machines. The research appears today in the journal Nature Communications.
"Most of the time, research in our field is about getting computers to think like people," says senior author Chaz Firestone, an assistant professor in Johns Hopkins' Department of Psychological and Brain Sciences. "Our project does the opposite -- we're asking whether people can think like computers."
In some cases, all it takes for a computer to call an apple a car, is reconfiguring a pixel or two. In other cases, machines see armadillos and bagels in what looks like meaningless television static.
To test this, Firestone and lead author Zhenglong Zhou, a Johns Hopkins senior majoring in cognitive science, essentially asked people to "think like a machine." Machines have only a relatively small vocabulary for naming images. So, Firestone and Zhou showed people dozens of fooling images that had already tricked computers, and gave people the same kinds of labeling options that the machine had. In particular, they asked people which of two options the computer decided the object was -- one being the computer's real conclusion and the other a random answer. (Was the blob pictured a bagel or a pinwheel?) It turns out, people strongly agreed with the conclusions of the computers.
Next, researchers upped the ante by giving people a choice between the computer's favorite answer and its next-best guess. (Was the blob pictured a bagel or a pretzel?) People again validated the computer's choices, with 91 percent of those tested agreeing with the machine's first choice.
Even when the researchers had people guess between 48 choices for what the object was, and even when the pictures resembled television static, an overwhelming proportion of the subjects chose what the machine chose well above the rates for random chance. A total of 1,800 subjects were tested throughout the various experiments.
"We found if you put a person in the same circumstance as a computer, suddenly the humans tend to agree with the machines," Firestone says. "This is still a problem for artificial intelligence, but it's not like the computer is saying something completely unlike what a human would say."
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