College of Sciences Distinguished Speaker Series:

Big Data & Virtual Classrooms: Reshaping How We Teach and Learn

Zhongzhou Chen, Ph.D.
Assistant Professor
UCF Department of Physics

Wednesday, March 25, 2020 at 6:00 p.m.
Tuscawilla Country Club
1500 Winter Springs Blvd.,
Winter Springs, FL 32708

Abstract

Can a new piece of technology change the way we teach and learn? This claim dates back to the invention of “educational-radio” in the 1920s. Yet most of the lecture halls today still look very much the same as those several hundred years ago. What is the evidence that the latest advancements in online learning technology is any different from all its predecessors, and holds the potential to finally change teaching and learning at scale?

In this talk Dr. Zhongzhou Chen will share his vision of how online learning and big data-based educational research could significantly re-shape some of the fundamental building blocks of teaching and learning today. Changes ranging from courses, textbooks, assessments, grades, to schools and credentials. Those changes can substantially transform the traditional course-based learning experience. This would be similar to taking an out dated public transportation system that moves passengers between fixed locations according to a pre-determined schedule, towards creating an “intelligent highway system” of learning. This system would give every student the opportunity to become the driver of their own learning process, guided by GPS signals created from big-data based learning research. 

Biography

Zhongzhou Chen, Ph.D., earned his doctorate in physics from University of Illinois Urbana Champaign in 2012, specializing in physics education and multimedia learning. In 2013 he joined the RELATE group at MIT as a postdoc, conducting educational research in Massive Open Online Courses (MOOCs) on the edX platform, mentored by Prof. David Pritchard.

His current research focuses on analyzing student learning data to improve the effectiveness of online learning and designing online-learning environments to enhance the quality of measurement and data collection.

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