Instructional aids to support a conceptual understanding of multiple representations.

Berthold, Kirsten; Renkl, Alexander

doi:10.1037/a0013247

Cited by 187 publications

(112 citation statements)

References 71 publications

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“…The tests contained 12 and 26 items respectively. The sensitivity and reliability of the test items have been established in recent years in a number of studies performed across Germany and The Netherlands (see e.g., Berthold and Renkl 2009;Eysink et al 2009;Gerjets et al 2009;Kolloffel et al 2009;Wouters et al 2007). The pre-test was aimed at measuring (possible differences in) the prior knowledge of the students.…”

Section: Representational Toolsmentioning

confidence: 99%

Comparing the effects of representational tools in collaborative and individual inquiry learning

Kollöffel

Eysink

Jong

2011

Computer Supported Learning

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Constructing a representation in which students express their domain understanding can help them improve their knowledge. Many different representational formats can be used to express one's domain understanding (e.g., concept maps, textual summaries, mathematical equations). The format can direct students' attention to specific aspects of the subject matter. For example, creating a concept map can emphasize domain concepts and forming equations can stress arithmetical aspects. The focus of the current study was to examine the role of tools for constructing domain representations in collaborative inquiry learning. The study was driven by three questions. First, what are the effects of collaborative inquiry learning with representational tools on learning outcomes? Second, does format have differential effects on domain understanding? And third, does format have differential effects on students' inclination to construct a representation? A pre-test post-test design was applied with 61 dyads in a (face-to-face) collaborative learning setting and 95 students in an individual setting. The participants worked on a learning task in a simulationbased learning environment equipped with a representational tool. The format of the tool was either conceptual or arithmetical or textual. Our results show that collaborative learners outperform individuals, in particular with regard to intuitive knowledge and situational knowledge. In the case of individuals a positive relation was observed between constructing a representation and learning outcomes, in particular situational knowledge. In general, the effects of format could not be linked directly to learning outcomes, but marked differences were found regarding students' inclination to use or not use specific formats.

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Section: Representational Toolsmentioning

confidence: 99%

Comparing the effects of representational tools in collaborative and individual inquiry learning

Kollöffel

Eysink

Jong

2011

Computer Supported Learning

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“…When IL is optimal and EL is low, learners can engage in knowledge elaboration processes (Kalyuga, 2009) like selfexplanation (Atkinson, Renkl, & Merrill, 2003;Berthold & Renkl, 2009) and argumentation (Fischer, 2002;Knipfer, Mayr, Zahn, Schwan, & Hesse, 2009) that impose GL and facilitate learning.…”

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confidence: 99%

Development of an instrument for measuring different types of cognitive load

et al. 2013

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According to cognitive load theory, instructions can impose three types of cognitive load on the learner: intrinsic load, extraneous load, and germane load. Proper measurement of the different types of cognitive load can help us understand why the effectiveness and efficiency of learning environments may differ as a function of instructional formats and learner characteristics. In this article, we present a ten-item instrument for the measurement of the three types of cognitive load. Principal component analysis on data from a lecture in statistics for PhD students (n = 56) in psychology and health sciences revealed a threecomponent solution, consistent with the types of load that the different items were intended to measure. This solution was confirmed by a confirmatory factor analysis of data from three lectures in statistics for different cohorts of bachelor students in the social and health sciences (ns = 171, 136, and 148), and received further support from a randomized experiment with university freshmen in the health sciences (n = 58).

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“…Thus, prompts for focused processing lead to discriminant effects: a positive effect on elaborations directed toward domain principles and on conceptual knowledge and simultaneously a negative effect on thinking about arithmetic operations during learning and on procedural knowledge. We call this pattern of results-where prompts have a positive as well as a negative effect-double-edged prompt effects (for another example of such an effect, see Berthold and Renkl 2009). These double-edged prompt effects might be due to the highly complex learning environment.…”

Section: Types Of Instructional Assistancementioning

confidence: 95%

How to Foster Active Processing of Explanations in Instructional Communication

Berthold¹,

Renkl

2010

Educ Psychol Rev

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In instructional communication settings, instructional explanations play an important role. Despite the very common use of instructional explanations, empirical studies show that very often, they have no positive effects on learning outcomes. This ineffectiveness might be due to mental passivity of the recipient learners that leads to shallow processing of the explanations. Against this background, we introduce several types of instructional assistance to foster active processing of written instructional explanations in asynchronous computer-mediated instructional communication settings. The findings of three experiments showed that prompts or training for focused processing regarding the central principles and concepts of the explanation are especially effective with respect to fostering learning outcomes. Keywords Asynchronous computer-mediated instructional communication . Written instructional explanations . Active processing . Focused processing In instructional communication settings, instructional explanations play an important role. The most prevalent activity in natural human tutoring is providing explanations (Chi et al. 2001). Instructional explanations are designed with the specific purpose of teaching (e.g., Leinhardt and Steele 2005) and communicating a particular aspect of the learning contents. Despite the very common use of instructional explanations, empirical studies show that very often, they have no positive effects on learning outcomes (e.g., Chi et al. 2001; VanLehn et al. 2003 for tutor settings; Webb 1989 for peer settings; for an overview, see Wittwer and Renkl 2008).This ineffectiveness might be partly attributed to problems on the part of the instructor in adapting his or her messages to the knowledge and needs of the learner or recipient. Wittwer, Nückles, and Renkl (this issue) proposed that instructional explanations should be tailored to an individual learner and developed an approach to provide instructors with information about a learner's understanding. In this way, the instructor is able to diagnose Educ Psychol Rev (2010) 22:25-40

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Instructional aids to support a conceptual understanding of multiple representations.

Cited by 187 publications

References 71 publications

Comparing the effects of representational tools in collaborative and individual inquiry learning

Comparing the effects of representational tools in collaborative and individual inquiry learning

Development of an instrument for measuring different types of cognitive load

How to Foster Active Processing of Explanations in Instructional Communication

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