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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. 

Understanding how novices acquire and hone visual search skills is crucial for developing and optimizing training methods across domains. Network analysis methods can be used to analyze graph representations of visual expertise. This study investigates the relationship between eye-gaze movements and learning outcomes among undergraduate dentistry students who were diagnosing dental radiographs over multiple semesters. We use network analysis techniques to model eye-gaze scanpaths as directed graphs and examine changes in network metrics over time. Using time series clustering on each metric, we identify distinct patterns of visual search strategies and explore their association with students’ diagnostic performance. Our findings suggest that the network metric of transition entropy is negatively correlated with performance scores, while the number of nodes and edges as well as average PageRank are positively correlated with performance scores. Changes in network metrics for individual students over time suggest a developmental shift from intermediate to expert-level processing. These insights contribute to understanding expertise acquisition in visual tasks and can inform the design of AI-assisted learning interventions. 

When students reflect on their learning from a textbook via think aloud, network representations can be used to capture their concepts and relations. What can we learn from these network representations about students’ learning processes, knowledge acquisition, and learning outcomes? This study brings methods from entity and relation extraction using classic and LLM-based methods to the application domain of educational psychology. We built a ground-truth baseline of relational data that represent relevant (to educational science), textbook-based information as a semantic network. We identified SPN4RE and LUKE as the most accurate method to extracting semantic networks capturing the same types of information from transcriptions of verbal student data. Correlating the students’ semantic networks with learning outcomes showed that students’ verbalizations varied in structure, reflecting different learning processes. Denser and more interconnected semantic networks indicated more elaborated knowledge acquisition. Structural features such as the number of edges and surface overlap with textbook networks significantly correlated with students’ posttest performance. 

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. 

It is well-known that a significant population of doctoral students drop out of their graduate programs and face or develop significant mental health distress. Stress plays a role in exacerbating mental health distress in both engineering PhD programs and more broadly for university students in general. While rates of dropout for engineering students may not differ strongly from other disciplines, engineering students have been suggested to be less likely to seek help from university services for well-being concerns. In the first year of our three‐year NSF RFE project, we interviewed doctoral engineering students to identify major stressors present in the doctoral engineering experience at the present study’s focal institution. In the second year of our project, we had developed the Stressors for Doctoral Students Questionnaire - Engineering (SDSQ-E), a novel survey which measures the frequency and severity of these top sources of stress for doctoral engineering students. The SDSQ-E was designed using the results of first year interviews and a review of the literature on stress for doctoral engineering students. In year three, we completed analysis of the year 3 data and conducted further testing of the SDSQ-E. We also developed a discipline-general form of the survey, called the SDSQ-G. In October-December 2023, we administered these surveys to engineering PhD students as a subset of a large sample of graduate students at two institutions. Further, we tested the potential for the SDSQ-E to predict factors such as anxiety, depression, or intention to persist in doctoral programs. We broadly summarize these survey distributions including tests of the SDSQ-E for validity, fairness, and reliability. 

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. 

Giving a voice to marginalized groups and understanding the double bind is critical, especially after the Charlotte, VA protests and the white supremacist discourse that has pervaded our country. The result of the discourse, more subtle beliefs about white superiority and institutional barriers is an overrepresentation of women of color (WOC) in the leaky STEM pipeline and thus the loss of their presence and expertise. The absence of WOC hinders knowledge production and innovation that is essential for societal advancements and scientific discovery. The “chilly climate” is often cited as an explanation for the loss of WOC from STEM. However, interactions that allow the “chilly climate” to persist have yet to be characterized. This lack of understanding can inhibit the professional engineering identity construction of WOC. Additionally, engineering education research typically focuses on a single identity dimension such as gender or socio-economic status. These studies connect an identity dimension to student outcomes and few studies clarify how the identity is situated within the social context of the engineering culture. Consequently, a need exist to examine how the engineering culture impacts multiple components of identity and intersecting identities of WOC. To address this gap, our study illuminates the intersections of identity of WOC and how they perceive the double bind of race and gender within the context of their engineering education. The data reported here are a part of a larger, sequential mixed-methods study (N=276) of undergraduate female engineering students at a large Midwestern research university. This project applies the framework of intersectionality with the following scales: Engineering Identity, Ethnic Identity, Womanist Identity, Microaggressions, and Depression. We use intersectionality to investigate the interaction between intersecting social identities and educational conditions. We introduce the Womanist Identity Attitude scale to engineering education research, which provides an efficient way to understand gender, racial, and intersecting identity development of WOC. We utilize the microaggressions scale, in order to develop quantitative measures of gender-racial discrimination in STEM and compare to previous research. We also included the Patient Health Questionnaire (PHQ-9), an instrument for measuring depression, to assess health outcomes of respondents’ experiences of gender-racial microaggressions. Our three emergent findings suggest instrument accuracy and provide insight into the identity and depression subscales. Factor analysis established a basis to refine our quantitative survey instruments, and indicated that 23 items could offer greater accuracy than the original 54 items instrument. Second, the majority of participants report a high level of identification with engineering. This result rebuffs the long-held stereotypes that females are less interested in engineering. Third, a significant portion of female respondents self-reported PHQ-9 scores in the 15-19 range, which corresponds with a “major depression, moderately severe” provisional diagnosis, the second-highest in severity in the PHQ-9 provisional diagnosis scale. These elevated levels of depression correlated significantly to frequent instances of microaggressions. These preliminary findings are providing never-before seen insight into the experiences of WOC in engineering. Our results suggest a path to accurately describe the experiences of WOC in engineering, while revealing options for improving inclusion efforts.