Eye Tracking in Assessing Computational Thinking
Tuesday, March 27 11:30 AM-12:30 PM
Presider:Laurie Sharp, West Texas A&M University, United States
Multiple methodologies have been utilized to assess learning of computational thinking (CT) including student-created artifacts, interviews with students about their artifacts, tests consisting of multiple-choice, fill-in-blank, open-ended items, jumbled blocks or lines to put in correct orders, and matching, debugging, and code tracing items, qualitative analyses of classroom observation, students’ grades and enrollment data, interviews with teachers, and design scenarios. However, these assessment methods do not provide a complete picture about students’ thinking process for a full report on their learning of CT. Eye-tracking technology may be used as an objective and complementary instrument giving information about students’ thinking process as well as technical progress as they are involved in each CT competency. However, no prior research examined eye-tracking technology as an assessment instrument in learning of core CT concepts and fluency of development in CT practices. Therefore, this study will present a novel assessment method utilizing eye-gaze data collected via eye-tracking technology.