Uncovering Student Mental Models of Linked Lists in Data Structures to Identify Their Misunderstandings

In computer science, learning abstract fundamental concepts that require students to understand memory management can be very difficult and lead to misunderstandings that carry on into the advanced topics. This is especially true in data structures, and understanding how novice students' think about data structures, after learning about them in class, is important for improving teaching and learning in computer science. More specifically, this research focuses on measuring students' mental models about linked lists and visual-spatial reasoning to uncover how undergraduate students reason about the lists and identify difficulties or misunderstandings students have.

Major Advisor: Jennifer Parham-Mocello
Committee: Margaret Burnett
Committee: Mike Bailey
Committee: Christopher A. Sanchez
GCR: Maggie Niess