The Structure of Academic Achievement: Searching for Proximal Mechanisms Using Machine Learning Techniques

• In Event: Hilton Roundtable Session 6
  In Roundtable Session: Machine Learning and Data Sciences

Abstract

Causal search algorithms have been effectively applied in different fields, including biology, genetics and neuroscience. However, there have been scant applications of these methods in social and behavioral sciences. This paper provides an illustrative example of how causal search algorithms can shed light on important social and behavioral problems by using these algorithms to find the proximal mechanisms of academic achievement. Using a nationally representative dataset with a wide range of relevant contextual and psychological factors, I implement four causal search procedures that varied important dimensions in the algorithms. Consistent with previous research, the algorithms identified prior achievement, executive functions and motivation as direct causes of academic achievement. I discuss the advantages and limitations of these methods for education research.

Author

• Rafael Quintana, University of Kansas