Learning with multiple representations: Extending multimedia learning beyond the lab

Eilam, Billie; Poyas, Yael

doi:10.1016/j.learninstruc.2007.07.003

Cited by 41 publications

(24 citation statements)

References 33 publications

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“…For example, in a classic study, Mayer and Gallini (1990) showed that students with low prior knowledge scored highest on conceptual recall and problem solving in conditions that combined text with illustrations. Recent findings support the benefit of combining visual and textual material to foster mental model development (Butcher 2006) and posttest retention (Eilam and Poyas 2008). To summarize, these studies indicate that the multimedia principle seems to foster the processing of essential material when people have low domain-specific knowledge.…”

Section: Visualizations As a Moderator Of The Size Of Expertise Diffementioning

confidence: 57%

Expertise Differences in the Comprehension of Visualizations: a Meta-Analysis of Eye-Tracking Research in Professional Domains

2011

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This meta-analysis integrates 296 effect sizes reported in eye-tracking research on expertise differences in the comprehension of visualizations. Three theories were evaluated: Ericsson and Kintsch's (Psychol Rev 102:211-245, 1995) theory of long-term working memory, Haider and Frensch's (J Exp Psychol Learn Mem Cognit 25:172-190, 1999) information-reduction hypothesis, and the holistic model of image perception of Kundel et al. (Radiology 242:396-402, 2007). Eye movement and performance data were cumulated from 819 experts, 187 intermediates, and 893 novices. In support of the evaluated theories, experts, when compared with non-experts, had shorter fixation durations, more fixations on task-relevant areas, and fewer fixations on task-redundant areas; experts also had longer saccades and shorter times to first fixate relevant information, owing to superiority in parafoveal processing and selective attention allocation. Eye movements, reaction time, and performance accuracy were moderated by characteristics of visualization (dynamics, realism, dimensionality, modality, and text annotation), task (complexity, time-on-task, and task control), and domain (sports, medicine, transportation, other). These findings are discussed in terms of their implications for theories of visual expertise in professional domains and their significance for the design of learning environments.Expertise in the comprehension of visualizations has gained growing attention over the past years (de Groot and

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Section: Visualizations As a Moderator Of The Size Of Expertise Diffementioning

confidence: 57%

Expertise Differences in the Comprehension of Visualizations: a Meta-Analysis of Eye-Tracking Research in Professional Domains

2011

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“…Ainsworth's (1999Ainsworth's ( , 2006 functional taxonomy suggested that different representation-types may complement each other, for example, by each presenting different processes or information that cannot be presented by another. However, multi-representational instruction may also hinder learning, mostly due to learners' difficulties to map analogical elements among these representations (Ainsworth 1999;Eilam and Poyas 2008;Tabachneck-Schijf and Simon 1998;Yerushalmy 1991), calling for further research to systematically examine additional ways to comprehensively promote students' understanding of feeding relations and systems.…”

Section: Using a Combination Of Instructional Modesmentioning

confidence: 99%

System thinking and feeding relations: learning with a live ecosystem model

Eilam

2011

Instr Sci

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Considering well-documented difficulties in mastering ecology concepts and system thinking, the aim of the study was to examine 9th graders' understanding of the complex, multilevel, systemic construct of feeding relations, nested within a larger system of a live model. Fifty students interacted with the model and manipulated a variable within it in the course of this model ecosystem yearlong inquiry, in a laboratory/traditional learning environment. Students' written responses to 10 pretest-posttest probes underwent fine-grain analysis regarding 53 descriptors of the system of feeding relations. Overall, students exhibited initial system thinking, manifested in different levels of increased ability to identify: system components, processes, levels, and their interrelations; ecosystem patterns and control mechanisms; equilibrium shifts; and spatial and temporal aspects of feeding relations. However, many still exhibited a deficient understanding of the system studied, reflecting a deficient system thinking. Implications for systemic ecology teaching and learning are discussed.Keywords System thinking Á Live system model Á Long-term inquiry Á Ecology instruction and learning Á Junior high school Instruction of ecology poses great difficulties for students' learning (e.g., Hogan and Fisherkeller 1996;Reiner and Eilam 2001). These difficulties are usually attributed to ecology's interdisciplinary nature, to ecological theories' low level of generality, formalization, and verifiability (del Solar and Marone 2001), to the complex systemic nature of ecosystems (Grotzer and Bel Basca 2003;Hmelo-Silver et al. 2007), to the need to comprehend biological systems of increasing levels of complexity (Model et al. 2005), and to students' prior knowledge (Vosniadou 1994). For most biology students, learning biology mostly involves memorizing details about static components of phenomena while disregarding systemic changes in time (Wilensky and Reisman 2006). Learning about a system's components alone does not ensure students' development of system thinking.

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“…Another area of focus is the extent to which interpretive constraint in a representation, such as graphic simplicity, helps or hinders student understanding, and under what conditions (Ainsworth 2008a, b, c ;Eilam and Poyas 2008 ) . Ainsworth ( 2008c ) claimed that too often students' simultaneous exposure to multiple representations make learning more diffi cult, and that students needed peer and teacher support in an effectively structured learning environment.…”

Section: Learning Through Interpreting Representationsmentioning

confidence: 99%