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Richard E. Mayer has made major contributions to Educational Psychology since the 1970s, including work on learning in mathematics, creativity, interest, measurement, problem solving, and especially multimedia learning, defined as learning from instructional material that includes information in both verbal and visual form. In a 2024 reflection, Mayer called for identifying boundary conditions—i.e., moderators of effects—of his multimedia design principles. In an effort to identify these, we meta-analyzed Mayer’s corpus of multimedia research. We searched Google Scholar, PsycINFO, and the Cambridge Handbook of Multimedia Learning 3rd Ed. for peer-reviewed articles on multimedia learning with Mayer as an author published 1990-2022 and located 92 articles reporting on 181 studies reporting on 591 separate effects. We coded for 9 moderators: multimedia design principle, multimedia type, age, academic domain, country/continent, treatment duration, dependent variable type, year, and authorship order. We analyzed the Hedge’s g effect sizes using a multilevel regression approach in the metafor package in R. The overall effect was g = 0.37, which was significantly moderated by all moderators, including a small decline in effect size per year. Mean effects by multimedia design principle were uneven, with the largest significant effects for removing seductive detail, modality principle, personalization, multimedia principle, sentence-level coherence, and self-explanation. Medium significant overall effects were found for the testing effect, scaffolding, cueing, and embodiment. Large, consistent effects were found for text + diagrams across factual, inferential, and transfer outcomes. Less-consistent effects were found for animation, games, and simulations, with smaller effects on factual learning and on average larger effects on inferential and transfer outcomes, but no significant effects for virtual reality. We identified two boundary conditions in tests of design principle x DV type interactions and Multimedia type x DV type interactions. We close by interpreting various findings in phases of Mayer’s work, characterized by collaborators and educational technologies. We also contextualize Mayer’s findings within recent meta-analyses of the larger published research on various design principles. 

Student during-learning data such as think-alouds or writing are often coded for use of strategies or moves, but less often for what knowledge the student is using. However, analyzing the content of such products could yield much valuable information. A promising technique for analyzing the content of student products is semantic network analysis, more widely used in political science, communication, information science, and some other social science disciplines. We reviewed the small literature on semantic network analysis (SemNA) of individuals with relevant outcomes to identify which network analysis metrics might be suitable. The Knowledge Integration (KI) framework from science education is discussed as focusing on amount and structure of student knowledge, and therefore especially relevant for testing with SemNA metrics. We then re-analyze three published think-aloud data sets from undergraduate students learning introductory biology with the metrics found in the literature review. Significant relations with posttest comprehension score are found for number of nodes and edges; degree and betweenness centrality; diameter, and mean distance. Inconsistent results possibly due to text-specific features were found for number of clusters, LCC, and density, and null results were found for PageRank centrality and centralization degree. Basic principles from the KI framework are supported—amount of information (nodes), connections (edges, average degree), key ideas (degree and betweenness centrality) and length of causal chains (mean distance and diameter) are related to posttest comprehension, but not density or LCC. Possible explanations for slight variations across data sets are discussed, and alternative theories and metrics are offered. 

Doctoral students experience high rates of mental health distress and dropout; however, the mental health and wellness of engineering doctoral students is understudied. Studies of student persistence, wellness, and success often aggregate fields together, such as by studying all engineering students. Thus, little work has considered the experiences of biomedical engineering (BME) doctoral students, despite differences between doctoral BME research, course content, and career expectations compared with other engineering disciplines. In this qualitative interview case study, we explore stressors present in the BME graduate experience that are unique from engineering students in other disciplines. Methods We analyzed a longitudinal interview study of doctoral engineering students across four timepoints within a single academic year, consisting of a subsample (n=6) of doctoral students in a BME discipline, among a larger sample of engineering doctoral students (N=55). BME students in the sample experienced some themes generated from a larger thematic analysis differently compared with other engineering disciplines. These differences are presented and discussed, grounded in a model of workplace stress. Results BME participants working in labs with biological samples expressed a lack of control over the timing and availability of materials for their research projects. BME participants also had more industry-focused career plans and described more commonly coming to BME graduate studies from other fields (e.g., another engineering major) and struggling with the scope and content of their introductory coursework. A common throughline for the stressors was the impact of the interdisciplinary nature of BME programs, to a greater extent compared with other engineering student experiences in our sample. Conclusions We motivate changes for researchers, instructors, and policymakers which specifically target BME students and emphasize the importance of considering studies at various unit levels (university department level vs college level vs full institution) when considering interventions targeting student stress and wellness. 

Reports on results from the first year of the RFE project, in which PhD engineering students were interviewed about stressors. 

Learning from multiple representations (MRs) is not an easy task for most people, despite how easy it is for experts. Different combinations of representations (e.g., text + photograph, graph + formula, map + diagram) pose different challenges for learners, but across the literature researchers find these to be challenging learning tasks. Each representation typically includes some unique information, as well as some information shared with the other representation(s). Finding one piece of information is only somewhat challenging, but linking information across representations and especially making inferences are very challenging and important parts of using multiple representations for learning. Coordination of multiple representations skills are rarely taught in classrooms, despite the fact that learners are frequently tested on them. Learning from MRs depends on the specific learning tasks posed, learner characteristics, the specifics of which representation(s) are used, and the design of each representation. These various factors act separately and in combination (which can be compensatory, additive, or interactive). Learning tasks can be differentially effective depending on learner characteristics, especially prior knowledge, self-regulation, and age/grade. Learning tasks should be designed keeping this differential effectiveness in mind, and researchers should test for such interactions.