Computerized adaptive testing (CAT) is an incredibly important innovation in the world of assessment. It’s a psychometric paradigm that applies machine learning principles to personalize millions and millions of assessments, from K12 education to university admissions to professional certification to employment screening to medical surveys. While invented in the 1970s, primarily at as part of a Defense research grant at the University of Minnesota, it remains a highly relevant and exciting topic today, especially with the advent of the cloud.
The International Association of Computerized Adaptive Testing (IACAT; www.iacat.org) was founded in 2009 at a small conference held at the University of Minnesota. Since then, it’s focused on being the nexus of adaptive testing resources and research. A key component of this mission is a biannual conference that rotates around the world. For 2017, the conference will take place at Niigata, Japan, 18-21 August.
Today, a draft of the program was released, so you can now see the scientifically rigorous and internationally-spanning speakers and topics. Click here to view the program. I’m honored to be able to present a paper with my colleague Jordan Stoeger, as well as teach a workshop with my good friend John Barnard from Australia (www.epecat.com). A big thanks to John, Cliff Donath, Tetsuo Kimura, Alper Sahin, and all others that have contributed time to making this conference the excellent meeting that it is.
If you are at all interested in CAT, or even assessment technology in general, I highly recommend that you consider attending this year’s conference. I hope to see you there!
Are you on social media? Use the hashtag #IACAT2017. Join our LinkedIn Group.