Computational psychometrics describes the blend of the analytical tools from the machine learning (ML) arsenal with cutting edge work in theoretical psychometric research. The new discipline computational psychometrics ( von Davier, 2015, 2017) sits squarely in the intersection of these fields. Many of these innovative ideas are in the various fields associated with education, learning, and assessment. Recent advances in computing technology have given us the tools to realize many innovative ideas previously beyond our grasp. This could be said of the state of learning and assessment systems in the current era. In his quote Gibson alludes primarily to the fact that progress is simply the spread of what is niche to something that is ubiquitous and equitable. The fiction author William Gibson said, “The future is already here, it is just not very evenly distributed” ( Rosenberg, 1992). The education that students receive is then primarily tailored to these groups as a one size fits all approach rather than a personalized and adaptive experience. Students are grouped into various hierarchical aggregations such as classrooms, grades, and schools. This traditional education system has been in place with almost no perceptible change since the dawn of the previous century. Many of the characteristics of today's classrooms would be familiar to our great-grandparents: A teacher lecturing to students sitting in rows of organized desks The teacher instructing from a prepared lesson plan, and the students listening attentively.
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