Is a Preference for Realism Really Naive After All? A Cognitive Model of Learning with Realistic Visualizations

Skulmowski, Alexander; Nebel, Steve; Remmele, Martin; Rey, Günter Daniel

doi:10.1007/s10648-021-09638-1

Cited by 24 publications

(22 citation statements)

References 72 publications

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“…Taken together, manipulating realism though differences in shading and rendering appears to lead to stronger effects on learning than altering the geometry. This conclusion might be of high relevance for the GSR model of realism (Skulmowski et al, 2021) and needs to be investigated further. In addition, the interaction effect between shape distinctness and the descriptiveness of label texts underlines that the integrated model of text and picture comprehension (Schnotz, 2014) already has a relevance when only a minimal amount of text is presented in a visualization.…”

Section: Discussionmentioning

confidence: 89%

“…The model includes a definition of realism in visualizations, called the geometry, shading, and rendering (GSR) model. When creating 3D visualizations, designers use geometry to generate the 3D shape of a model (Skulmowski et al, 2021). The GSR model describes that the resolution of the geometry can range from blocky approximations to highly detailed objects featuring even minute detail.…”

Section: Introductionmentioning

confidence: 99%

“…In the cognitive model of learning with realistic visualizations, the components of GSR can contribute towards the perceptual load of a visualization (Skulmowski et al, 2021). In the model, a higher complexity of the geometry, details generated using shading, and certain rendering effects are thought to bring about more elements in the mental representations formed by learners.…”

Section: Introductionmentioning

confidence: 99%

“…In the model, a higher complexity of the geometry, details generated using shading, and certain rendering effects are thought to bring about more elements in the mental representations formed by learners. The amount of elements is thought to be associated with the cognitive load placed on learners as they are generating a mental schema in long‐term memory (Skulmowski et al, 2021). Importantly, different types of tests, such as retention and transfer tests, are considered to tap into different knowledge bases that may be differently impacted by different levels of perceptual richness (Skulmowski et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…The amount of elements is thought to be associated with the cognitive load placed on learners as they are generating a mental schema in long‐term memory (Skulmowski et al, 2021). Importantly, different types of tests, such as retention and transfer tests, are considered to tap into different knowledge bases that may be differently impacted by different levels of perceptual richness (Skulmowski et al, 2021). In the following, this aspect of interactions between realism and the types of tests used will be discussed.…”

Section: Introductionmentioning

confidence: 99%

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Realistic visualizations can aid transfer performance: Do distinctive shapes and descriptive labels contribute towards learning?

Skulmowski

2022

Computer Assisted Learning

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Background: Realistic visualizations have been associated with benefits in retention performance, but also with disadvantages in transfer tasks. Realistic details are considered to be helpful for the initial learning of content, but not for the generalization to other domains and tasks.Objectives: The contradictory nature of previous evidence needed to be tested in a situation in which both the retention of concrete surfaces as well as the transfer to different shapes is necessary.Methods: Three experiments were conducted to assess whether transfer tasks in which knowledge of surfaces needed to be transferred to variations can benefit from realism, shape distinctness, and descriptive labels. In Experiment 1, participants learned skull anatomy either in a schematic or a realistic style and completed near and far transfer tests. In Experiment 2, the realism and shape distinctness of a fictional bone model was varied. Subjective cognitive load, retention performance, and transfer scores were measured. Experiment 3 compared how distinct and less distinct shapes of another fictional bone model affect learning in combination with label texts of differing semantic descriptiveness. Results and Conclusions:In Experiment 1, performance in an anatomy learning task was raised by learning with a realistic visualization across near and far transfer tests.Experiment 2 revealed that extraneous cognitive load is increased for realistic visualizations when the depicted shapes lack distinctness. Experiment 3 suggests that when realistic visualizations feature less distinct shapes, learners struggle with descriptive labels in retention tests and benefit from similarly unspecific labels.

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Section: Discussionmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Realistic visualizations can aid transfer performance: Do distinctive shapes and descriptive labels contribute towards learning?

Skulmowski

2022

Computer Assisted Learning

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The impact of instructional support via generative learning strategies on the perception of visual authenticity, learning outcomes, and satisfaction in AR-based learning

Moser,

Lewalter

2024

Eur J Psychol Educ

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Augmented reality (AR) presents significant opportunities for creating authentic learning environments by accurately mirroring real-world objects, contexts, and tasks. The visual fidelity of AR content, seamlessly integrated into the real world, contributes to its perceived authenticity. Despite acknowledging AR’s positive impact on learning, scant research explores specific learning strategies within an AR context, and there’s a lack of studies linking perceived visual authenticity to these strategies. This study addresses these gaps by surveying learners using AR technology to study the human cardiovascular system, exploring perceived visual authenticity, learning outcomes, and satisfaction. Learners used either (1) AR with the self-explanation learning strategy, (2) AR with the drawing learning strategy, or (3) AR only. Analysis of variance and correlation was used for data analysis. Results indicated no significant differences in perceived visual authenticity and satisfaction among the learning strategy groups. However, groups employing learning strategies showed superior learning outcomes compared to the AR-only group. Crucially, the self-explanation learning strategy significantly enhanced knowledge gain compared to drawing and AR-only groups, indicating that self-explanation, together with the visual input from the AR-learning environment, fosters a more coherent mental representation. This increased learning efficacy was achieved while maintaining a consistent perception of visual authenticity and satisfaction with the learning material. These findings expand the current landscape of AR research by moving beyond media comparison studies.

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Realistic details impact learners independently of split-attention effects

Skulmowski

2023

Cogn Process

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Realistic visualizations are considered to introduce the risk of distracting learners from relevant information. In two experiments, the interplay between realism and a known form of distraction, the split-attention effect, were investigated. This effect describes that spatially separating relevant information can have a substantial negative effect on learning. The experiments were conducted using short anatomy learning tasks to test whether a combination of realism and split attention would lead to the worst retention performance or, alternatively, whether realism can counteract the negative effects of split attention. The first experiment (n = 125) revealed that realism attenuated the cognitive load induced by split attention, suggesting a compensatory effect of realism (i.e., realism may have helped learners to deal with the detrimental influence of split attention). However, retention performance was not impacted in a similar way, indicating that this compensatory effect on subjective cognitive load may actually be the result of learners’ illusion that realistic details are helpful. Split attention significantly reduced retention performance. Experiment 2 (n = 152) resulted in negative effects of realism and split attention on retention. In sum, the experiments suggest that realistic details can affect learners independently of other visual design factors as exemplified by the split-attention effect. Thus, the assumption that realism is likely to distract learners is rendered implausible by the experiments, as the distraction of split attention should have amplified any distractive potential of realistic details. However, the results also suggest that the effects of realism on learning are still somewhat unpredictable.

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Is a Preference for Realism Really Naive After All? A Cognitive Model of Learning with Realistic Visualizations

Cited by 24 publications

References 72 publications

Realistic visualizations can aid transfer performance: Do distinctive shapes and descriptive labels contribute towards learning?

Realistic visualizations can aid transfer performance: Do distinctive shapes and descriptive labels contribute towards learning?

The impact of instructional support via generative learning strategies on the perception of visual authenticity, learning outcomes, and satisfaction in AR-based learning

Realistic details impact learners independently of split-attention effects

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