More Nuanced Media Comparisons: Accounting for Complexity Through Moderators and Mediators

• In Event: Media Comparisons: To Do or Not to Do, That is the Question

Abstract

To advance media comparison research, more nuanced and theoretically grounded study designs are needed: Designs that move beyond simple comparisons of learning outcomes across media and instead account for the complexity of learning processes. This includes the systematic integration of mediator and moderator variables that shape the relationship between media use and learning.

Theoretical frameworks such as the Cognitive Affective Model of Immersive Learning (CAMIL; Makransky & Petersen, 2021) and the Cognitive Affective Social Theory of Learning in Digital Environments (CASTLE; Schneider et al., 2022) emphasize that media effects are not uniform but result from multicausal interactions between cognitive, affective, task-related, and contextual factors. Mediator variables such as presence, motivation, or cognitive load help explain how media influence learning outcomes, while moderator variables like prior knowledge or spatial ability determine for whom and under which conditions specific media are effective.

Moreover, moderated mediation designs allow for the investigation of how mediation processes themselves may vary depending on learner characteristics or contextual variables.
This contribution outlines methodological principles for designing media comparison studies that reflect this complexity. Key recommendations include: (1) standardizing instructional methods and content across conditions, (2) transparent description of learning designs, and (3) employing analytical approaches that test for interaction and mediation effects (e.g., structural equation modeling or mediation analysis using R packages such as mediation (Imai et al., 2010) or moderate.mediation (Qin & Wang, 2024) to capture indirect and conditional effects accurately).

Examples from research on immersive technologies illustrate how such designs can yield theoretically and practically relevant insights. For instance, Taçgın (2020) demonstrated in a learner-treatment interaction study that the perceived effectiveness of immersive virtual environments was moderated by learners’ prior knowledge. Klingenberg and colleagues (2022) illustrated in a value-added study that integrating generative learning activities such as segmentation and summarization into VR instruction enhanced transfer performance, underscoring the importance of instructional design as an active component within media treatments.

These examples demonstrate how media comparison studies can move beyond binary contrasts to contribute a more differentiated understanding of how, when, and for whom media support learning. When guided by theory, designed with methodological precision, and analyzed using appropriate statistical methods, media comparisons can yield insights that are both explanatory and actionable.

Author

• Miriam Mulders, University of Duisburg-Essen