Equations in Scientific Proofs: Effects on Comprehension

Dee-Lucas, Diana; Larkin, Jill H.

doi:10.2307/1163153

Cited by 7 publications

(9 citation statements)

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“…Yet, empirical findings about the relation between representational format, cognitive load, and learning outcomes are far from straightforward: the advantage of a particular format in learning is not universal, but depends on a complex interaction among the nature of the task and the material, the student's ability, prior knowledge, and practice time. For example, Dee-Lucas and Larkin (1991) found that learning with verbal material was superior to learning with equivalent equations. Leung et al (1997) replicated these findings but also found that this effect only applies to less able students.…”

Section: Representational Format and Cognitive Loadmentioning

confidence: 99%

The effects of representational format on learning combinatorics from an interactive computer simulation

et al. 2008

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The current study investigated the effects of different external representational formats on learning combinatorics and probability theory in an inquiry based learning environment. Five conditions were compared in a pre-test post-test design: three conditions each using a single external representational format (Diagram, Arithmetic, or Text), and two conditions using multiple representations (Text + Arithmetic or Diagram + Arithmetic). The major finding of the study is that a format that combines text and arithmetics was most beneficial for learning, in particular with regard to procedural knowledge, that is the ability to execute action sequences to solve problems. Diagrams were found to negatively affect learning and to increase cognitive load. Combining diagrams with arithmetical representations reduced cognitive load, but did not improve learning outcomes.

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Section: Representational Format and Cognitive Loadmentioning

confidence: 99%

The effects of representational format on learning combinatorics from an interactive computer simulation

et al. 2008

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“…When asked whether his knowledge of thermoconductivity would change his prediction, he said "no"; his prediction was consistent with molecular kinetic theory (Linn et al, in press). diSessa reports similar outcomes when physicists are asked to use scientific principles to explain the operation of vacuum cleaners and other common appliances (diSessa, 1983). If scientists cannot explain everyday phenomena in domains where they understand the great ideas, they are likely to be equally unsuccessful after taking a course introducing great ideas in other disciplines.…”

Section: Science Mattersmentioning

confidence: 91%

“…The heat-flow model and other pragmatic principles have proven helpful in guiding students' discovery and subsequent understanding of scientific phenomena. diSessa reports similar benefits from using computer microworlds to help students integrate their intuitive ideas about motion (diSessa, 1988). Fundamentally, pragmatic principles for middle school students provide abstractions that are macroscopic rather than microscopic and qualitative rather than quantitative.…”

Section: The Computer As Lab Partnermentioning

confidence: 95%

Science education reform: Building on the research base

Linn

1992

J Res Sci Teach

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Those concerned with science education are united in calling for reform, yet divided in specifying the nature of this reform. Several recent conferences (Gardner et al., 1990; Linn, 1987b; Shymansky & Kyle, 1992) have focused on reform as a joint collaborative process involving experts in cognition, science education, precollege teaching, the subject matter disciplines, teacher preparation, and curriculum design. This article (a) contrasts several perspectives on science education reform by examining recommendations for introductory science courses, (b) identifies research findings that can guide reform, (c) discusses the impact of research on national precollege curriculum reform, and (d) offers a set of recommendations to guide research and practice concerned with science education reform.

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“…The studies [48], [49], which considered the content domains of Pascal's fluid pressure principle and simple interest calculation, have examined representations in the form of words, equations, or combinations thereof and compared the effectiveness of these representations with each other, but not with representations involving pictures/graphs. Note that if the assumptions of the outlined theory of cognition hold true, then these representations involving equations are processed in the verbal channel and do not utilize the resources of the pictorial channel.…”

Section: Related Workmentioning

confidence: 99%

Computer-Based Instruction on Multimedia Networking Fundamentals: Equational Versus Graphical Representation

2005

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Abstract-Multimedia networking has been emerging in recent years as a strong driving force behind the expansion of the Internet. However, this topic is not commonly covered in the already content-intensive introductory networking courses. To facilitate student self-study of this novel topic the authors have developed a computer-based instructional module on the fundamentals of multimedia networking. In this paper, they describe the design and development of the module, which is aligned with Gagne's theory of instruction. They have developed two versions of the module-one with equation-based representation of the learning content and one with graph-based representation of the learning content. They have evaluated the two versions of the module with a total of 75 undergraduate, senior-level electrical engineering students, of which half were randomly assigned to the equational representation, and the other half to the graphical representation. They found that the graphical representation results in statistically significantly higher student performance on practice and post-test problems, shorter learning time, and more positive attitudes toward the computerbased instructional module.

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Equations in Scientific Proofs: Effects on Comprehension

Cited by 7 publications

References 0 publications

The effects of representational format on learning combinatorics from an interactive computer simulation

The effects of representational format on learning combinatorics from an interactive computer simulation

Science education reform: Building on the research base

Computer-Based Instruction on Multimedia Networking Fundamentals: Equational Versus Graphical Representation

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