Date Available


Year of Publication


Degree Name

Doctor of Philosophy (PhD)

Document Type

Doctoral Dissertation


Communication and Information



First Advisor

Dr. Derek R. Lane


Connecting the relevance of course content to students’ lives has been a learning strategy for decades. In educational psychology, Keller (1983) suggested content relevance to be a component within the ARCS model to motivate students toward learning behaviors. Within instructional communication research, Frymier and Shulman (1995) argued that students enter classrooms with the expectation that they will understand the connection between the content and their lives. Specifically, students want to know why they are taking a course and how it impacts their interests, needs, and professional goals (Frymier, 2001). In both education and instructional communication literature, teacher content relevance strategies are known to influence students’ learning behaviors. However, the influence of content relevance messages on students’ cognitive learning has been a missing link in extant research. Building upon previous theoretical framework, this dissertation extends the content relevance research agenda by investigating the extent to which students’ perceptions of instructional message content relevance and students’ experienced cognitive load predicts students’ cognitive learning. Data was collected from 559 undergraduate statistics students who completed an online survey about their perceptions of message content relevance, affect toward the instructor and the class, experienced cognitive load (intrinsic, extraneous, and germane), academic performance, and perceived cognitive learning. Results revealed a regression model explaining 11.1% of the total variance in students’ academic performance and 63.8% of the total variance in students’ perceptions of cognitive learning. Further, the full sample (N = 559) was divided by a median split to determine how low (n = 277) and high (n = 282) categories of content relevance interact with cognitive load, students’ affective behaviors, and learning strategies to predict academic performance and perceived cognitive learning. Analyses revealed significant models for low message content relevance regressed on academic performance explaining 18.2% of the total variance, and for high content relevance regressed on academic performance explaining 7.9% of the total variance. For low and high content relevance categories regressed on perceived cognitive learning, analyses revealed significant models accounting for 61% (low) and 40.3% (high) of the total variance. Implications of the results are presented in the discussion and conclusion.

Digital Object Identifier (DOI)