A Layered Model to Address Complexities in Multimedia Assessment Research and Design (LAMMP

Description

A Layered Model to Address Complexities in Multimedia Assessment Research and Design (LAMMP)

The widespread adoption of computer-based assessments has increased the use of multimedia (i.e., text and pictures), offering potential advantages like enhanced authenticity, accessibility, and validity. Despite these benefits, clear design guidelines for multimedia assessment are lacking, complicating both research and practice (Kirschner et al., 2017). Current empirical findings regarding multimedia’s effects on test performance and cognitive processing are inconsistent (e.g., Arts et al., 2024; Lindner, 2022), largely due to variability in multimedia features and the absence of shared definitions and operationalisations (Lindner, 2022).

To address these challenges, we developed LAMMP, a layered model for multimedia in assessment, based on a comprehensive literature review. It integrates insights from multimedia learning, test design, visual design, and cognitive psychology, and organises multimedia assessment into four layers: Learning and Assessment Context, Assessment Item, Multimedia, and Picture (Figure 1). Each layer describes key characteristics that influence the cognitive processes involved in understanding and resolving multimedia assessment items, while emphasizing their interdependencies (Figure 2).

A novel aspect of LAMMP is its explicit classification of multimedia’s purpose in assessments, addressing a gap in current research. Many studies focus on multimedia effects without considering why it was included (Kirschner et al., 2017). LAMMP distinguishes between different purposes: efficiency, effectiveness, enjoyability, and accessibility, and suggests that these purposes should guide the design and function of multimedia elements in assessments.

Figure 1

Layered Model LAMMP

Figure 2

Characteristics Layers LAMMP

The LAMMP Model

The Learning and Assessment Context (L) layer represents the broader context in which multimedia assessments occur. Alignment between this layer and the inner layers is essential for creating valid assessments and developing effective multimedia design guidelines. This layer includes assessee characteristics (e.g., prior knowledge, special educational needs, cultural background), assessment context (e.g., content, delivery conditions), and learning context (e.g., learning activities).

The Assessment Item (A) layer describes relevant design characteristics related to the item’s components (i.e., stem, question, answer area), cognitive processes required to resolve the item, and knowledge types it measures (e.g., apply, analyse, evaluate; factual, conceptual). Furthermore, it describes characteristics related to the text in the item, such as text type, modality, and linguistic complexity.

The Multimedia (MM) layer focuses on the combined use of verbal and pictorial representations in multimedia assessment items (Mayer, 2022; Schnotz, 2022). This layer describes design-related characteristics that may affect cognitive processing, such as coherence and signalling (Mayer, 2022), and addresses the reasons behind multimedia use in assessment. LAMMP distinguishes between purpose, which explains why multimedia is used (e.g., increasing accessibility or efficiency), function, which explains how this purpose can be achieved (e.g., through complementary representations that provide distinct types of support; Ainsworth, 2006), and relation type, which explains what the connection is between the pictorial and verbal content (e.g., decorative, representational; Lindner, 2022).

The Picture (P) layer focuses on picture content and design. LAMMP defines a picture as a representation that always contains at least one pictorial element and may include verbal elements. In such cases, the picture becomes a multimedia entity embedded within the overall multimedia item. If information provided by a picture is essential (i.e., necessary to solve the

item), it should be specified whether this information is conveyed through pictorial elements, verbal elements, or both, as this affects cognitive processing and problem-solving.

Other characteristics to consider regarding pictures, and more specifically their pictorial elements, include picture type (e.g., diagram, map), dynamism (i.e., static or dynamic), degree of realism (e.g., photographs versus schematic drawings), and colour. Any verbal elements within pictures should be evaluated similarly to text.

Conclusion and Discussion

LAMMP offers both theoretical and practical contributions. It provides a structured overview of key characteristics in multimedia assessment and clarifies those that have previously been inconsistently defined or operationalized in the literature (e.g., whether pictures include only pictorial elements or also verbal elements, and how essential information is conveyed). Additionally, it highlights gaps in current research, particularly regarding the purpose of multimedia in assessments. Addressing these gaps is essential for developing effective, evidence-based guidelines for multimedia assessment.

Practically, LAMMP can guide the design of multimedia assessments, offering structure and support while comprehensive guidelines are still being developed. For instance, the model’s focus on assessee characteristics can help practitioners design assessments that use multimedia to better accommodate specific learner needs and improve accessibility.

LAMMP should be seen as a starting point—a dynamic model capable of evolving as our research field advances. While it may already seem elaborate, we believe further elaboration could be useful in the future, particularly by incorporating additional functions of multimedia specific to assessment, such as supporting knowledge retrieval from long-term memory. We invite scholars in the field to contribute to its continuous development and refinement, allowing it to evolve into a robust, flexible framework that paves the way for innovative assessment practices.

References:

Arts, J., Emons, W., Dirkx, K., Joosten-ten Brinke, D., & Jarodzka, H. (2024). Exploring the multimedia effect in testing: The role of coherence and item-level analysis. Frontiers in Education, 9:1344012. https://doi.org/10.3389/feduc.2024.1344012

Ainsworth, S. (2006). DeFT: A conceptual framework for considering learning with multiple representations. Learning and Instruction, 16(3), 183-198. https://doi.org/10.1016/j.learninstruc.2006.03.001

Kirschner, P. A., Park, B., Malone, S., & Jarodzka, H. (2017). Toward a cognitive theory of multimedia assessment (CTMMA). In M. J. Spector, B. B. Lockee, & M. D. Childress (Eds.) Learning, Design, and Technology. An International Compendium of Theory, Research, Practice, and Policy (pp. 1-23). Springer. https://doi.org/10.1007/978-3-319-17727-4_53-1

Lindner, M.A. (2022). Principles for educational assessment with multimedia. In R. E. Mayer & L. Fiorella (Eds). The Cambridge Handbook of Multimedia Learning (3rd ed., pp. 552-565). Cambridge University Press. https://doi.org/10.1017/9781108894333

Mayer, R. E. (2022). Cognitive theory of multimedia learning. In R. E. Mayer & L. Fiorella (Eds.), The Cambridge Handbook of Multimedia Learning (3rd ed., pp. 57-72). Cambridge University Press. https://doi.org/10.1017/9781108894333

Schnotz, W. (2022). Integrated model of text and picture comprehension. In R. E. Mayer & L. Fiorella (Eds.), The Cambridge Handbook of Multimedia Learning (3rd ed., pp. 82- 99). Cambridge University Press. https://doi.org/10.1017/9781108894333

Period	28 Aug 2025
Event title	EARLI 2025: Realising Potentials through Education: Shaping the Minds and Brains for the Future
Event type	Conference
Location	Graz, AustriaShow on map
Degree of Recognition	International