Latent Semantic Analysis and Concept Inventories; Discovering and Classifying Student Misconceptions in STEM Education – Isidoros Doxas (BAE Systems)
Baltimore, MD 21218
USA
Abstract
Latent Semantic Analysis (LSA) is a vector-based bag-of-words model originally intended for use in information retrieval systems, which has found use in a wide range of pure and applied settings, from providing feedback to pilots on landing technique to diagnosing mental disorders from prose. Education applications of LSA include selecting instructional materials for individual students, grading student essays, improving student reading comprehension, and facilitating Concept Inventory construction.
Concept Inventories are multiple choice instruments that can provide researchers with a map of students’ conceptual understanding of a field. They are extensively used in all areas of Science, Technology, Engineering and Mathematics (STEM) education, but their construction is significantly hampered by the need to discover student’s particular misconceptions in each field. I will give a short introduction to LSA and to the use and construction of Concept Inventories, and I will describe how we use LSA to discover and classify student misconceptions, accelerating the construction and validation of these expensive instruments.
Work performed at the University of Colorado, Boulder. Current Address: BAE Systems, Columbia, MD.
Biography
Isidoros Doxas received a B.Sc. in Physics from Queen Mary College, University of London in 1981, a M.A. in Physics from Columbia University in New York in 1983, and a Ph.D. in Plasma Physics from the University of Texas at Austin in 1988. He spent 19 years at the University of Colorado, Boulder, first at the Astrophysical, Planetary and Atmospheric Sciences Department, and then at the Center for Integrated Plasma Studies, where he was a Fellow. He moved to BAE Systems in 2008. He has worked on plasma turbulence, nonlinear dynamics and chaos in fusion devices and space plasmas. His work is mainly analytical and numerical, and he has developed or co-developed novel numerical techniques for high performance computing in turbulence, forecasting, and detector technology. He has worked on science education since 1996. Since 2001 he has worked on intelligent tutoring systems and machine learning, which has led to his current interest in the geometry and dynamics of discourse and of people’s visual experience.
