Name: Sibley F. Lyndgaard

Ph.D. Dissertation Defense Meeting

Date: Wednesday, November 20, 2024

Time: 12:00 pm

Location: Virtual, Click here to join (Meeting ID: 237 548 020 532; Passcode: xyhojs)

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. The present study examined the relative contributions of distal trait and proximal strategic variables to explaining individual differences on an assessment of technological fluency. While item-level floor effects limited the interpretability of aggregate performance data, there were several interesting findings related to individual differences in process variables. Cluster analyses, for example, showed that minimally satisficing participants (who had very low intrapersonal variability in item scores) tended to be more positively oriented toward technology, while the two clusters of participants with greater intrapersonal variability in item scores could be distinguished by patterns of differences in both ability and non-ability variables. Follow-up qualitative analyses identified several key process variables for which ability differences were most striking, including inference quality (including the ability to self-correct faulty inferences), the ability to leverage visual problem cues, and the nature of AI use during problem-solving. Finally, exploratory regression analyses suggest that proximal strategic variables do improve prediction of available process indicators above and beyond distal trait complexes, providing support for a key proposition of the study. In addition to providing methodological recommendations for the assessment of technological fluency (e.g., more direct assessment of metacognitive processes, inclusion of domain knowledge tests), the present study’s findings suggest that the quality of metacognitive processing in technology-supported problem-solving may be correlated with individual differences in ability – this is a critical direction for future research.