Name: Sibley F. Lyndgaard

Ph.D. Dissertation Proposal Meeting

Date: Wednesday, January 10, 2024

Time: 3:15 pm

Location: Virtual, click here to join (Meeting ID: 234 379 475 426; Passcode: NduUrf)

Dissertation Chair/Co-Advisor:

Phillip L. Ackerman, Ph.D. (Georgia Tech)

Dissertation Committee Members:

Ruth Kanfer, Ph.D. (Co-Advisor, Georgia Tech)

Sashank Varma, Ph.D. (Georgia Tech)

Richard Catrambone, Ph.D. (Georgia Tech)

Margaret E. Beier, Ph.D. (Rice University)
 

Title: Distal Trait and Proximal Strategic Predictors of Technological Fluency  

Abstract: Modern workplaces are characterized by the necessity of just-in-time, non-routine problem-solving, often aided by technology. Although the Internet is an increasingly core resource for such problem-solving, the competencies which support leveraging technology for problem-solving are not well understood. Effective use of online resources depends upon a complex of individual differences including ability and non-ability traits, the accumulation of relevant knowledge and skills, and the use of adaptive strategic approaches during interactions with technological resources. I refer to this as technological fluency, a trait complex which describes individuals’ propensity (i.e., ability/willingness) to leverage technological resources to solve real-world problems. I propose to conduct an empirical study assessing individual differences in/psychological predictors of technological fluency, which will make three primary contributions. First, the delineation of this construct constitutes a theoretical contribution to the literature on real-world problem-solving performance, which is particularly important as informational technology resources continue to be integral to 21st century work and adult life. A criterion measure of technological fluency will be designed to avoid task-based, technology-based, or scoring limitations of assessments previously used to assess conceptually related constructs such as digital literacy. Second, I will develop/adapt a novel set of individual-difference measures representing learners’ strategic approaches to engaging online resources during problem-solving. Finally, I will demonstrate the utility of these measures for predicting individual differences in technological fluency above and beyond traditional approaches to predicting task performance.