Eye tracking to determine cognitive load during programming practice – UROP Spring Symposium 2021

Eye tracking to determine cognitive load during programming practice

Pari Shah


Pronouns: she/her

Research Mentor(s): Barbara Ericson, Assistant Professor
Research Mentor School/College/Department: Computing, School of Information
Presentation Date: Thursday, April 22, 2021
Session: Session 6 (4pm-4:50pm)
Breakout Room: Room 18
Presenter: 2

Event Link


Novice programmers need scaffolded instruction to maximize their ability to learn how to program. Parsons problems are an increasingly popular solution. These problems require learners to place mixed-up code blocks in the correct order to solve a problem. We are conducting think-aloud sessions and a within-subjects experiment to understand the efficiency and cognitive load of solving adaptive Parsons problems versus writing the equivalent (isomorphic) code. We are also investigating the impact of prior programming experience on students’ experiences and changes in students’ self-efficacy. This study will report on cognitive load and self-efficacy ratings before and after the task for the two problem times. We expect students to exhibit greater learning gains on fixing code with errors when solving Parsons problems with distractors than without. We also expect to find a correlation between self-efficacy and cognitive load ratings. The implications of this study are to improve programming learning tools for novice programmers.

Authors: Pari Shah
Research Method: Computer Programming

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