Reconsidering simulations in science education at a distance: features of effective use

Blake, Canan; Scanlon, Eileen

doi:10.1111/j.1365-2729.2007.00239.x

Cited by 98 publications

(81 citation statements)

References 20 publications

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“…In a second condition, the variables were integrated but unlinked, and, in a third, they were both unlinked and unintegrated. The authors found that students who learned the most were best able to transfer their new knowledge to new problems, and that these same students reported the least difficulty with the version that was both linked and integrated-a finding that expands upon the design principle of ''multiple representations'' (Blake and Scanlon 2007). However, intergroup differences emerged only when more challenging problems were presented to the students.…”

Section: Introductionmentioning

confidence: 88%

Using the Bifocal Modeling Framework to Resolve “Discrepant Events” Between Physical Experiments and Virtual Models in Biology

2016

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In this paper, we investigate an approach to supporting students' learning in science through a combination of physical experimentation and virtual modeling. We present a study that utilizes a scientific inquiry framework, which we call ''bifocal modeling,'' to link student-designed experiments and computer models in real time. In this study, a group of high school students designed computer models of bacterial growth with reference to a simultaneous physical experiment they were conducting, and were able to validate the correctness of their model against the results of their experiment. Our findings suggest that as the students compared their virtual models with physical experiments, they encountered ''discrepant events'' that contradicted their existing conceptions and elicited a state of cognitive disequilibrium. This experience of conflict encouraged students to further examine their ideas and to seek more accurate explanations of the observed natural phenomena, improving the design of their computer models. IntroductionThe nature and role of school science laboratories have been subject to widespread controversy in the research community (NRC 1996), especially regarding the benefits of physical, virtual, and combined laboratories (Olympiou and Zacharia 2012;Triona and Klahr 2003;Zacharia 2007). The popularity of simulation environments such as PhET (Perkins et al. 2006) has led policy-makers and scholars to question the real value of physical laboratories-especially in the face of the associated costs and logistics. A wave of research studies within the past 10 years has explored: (a) What are the advantages of physical laboratories relative to virtual laboratories and manipulatives, (b) whether the latter can replace the former (Triona and Klahr 2003), and (c) in what ways virtual models can simulate complex phenomena and permit student experimentation in domains that might otherwise be costly, impractical, or dangerous (Finkelstein et al. 2005;Jaakkola and Nurmi 2008;Jaakkola et al. 2011;Klahr et al. 2007; Perkins et al. 2006;Resnick and Wilensky 1998).The literature comparing hands-on or physical models (PM) with virtual models (VM) for science learning has sought to establish rules for choosing one modality over the other or for ordering them as distinct phases in a sequential process (de Jong et al. 2013). Zacharia and Anderson (2003) found that combining physical and virtual models increased teachers' learning of content knowledge in physics. Zacharia and Constantinou (2008) virtual laboratories led to greater conceptual understandings than did either type singly. For example, Liu (2006) compared groups of female high school students utilizing computer simulations and/or hands-on laboratory activities in chemistry. Controlling for time-on-task, the combination of both PM and VM was more effective than either option alone. But interesting interactions between content learning and epistemology were observed for this composite approach: There was a correlation between students' understanding o...

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Section: Introductionmentioning

confidence: 88%

Using the Bifocal Modeling Framework to Resolve “Discrepant Events” Between Physical Experiments and Virtual Models in Biology

2016

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“…Computer assistance has the potential to reduce the complexity of phenomena, to focus on a smaller set of items and variables, and to offer a 'model progression' over several levels . Further measures support successful learning with simulations, among them the use of multiple representations, 'tailorability' of tools (Blake & Scanlon, 2007), prompting for reflective activities (Pilkington & Parker-Jones, 1996;White & Frederiksen, 1998), and interpretative cues (Reid, Zhang, & Chen, 2003) projections. Additionally, the project gives students a short list of appropriate web links where they can deepen the knowledge gained so far.…”

Section: Tools Supporting Investigationsmentioning

confidence: 99%

Collaborative Inquiry Learning: Models, tools, and challenges

Bell

Urhahne²,

Schanze

et al. 2009

International Journal of Science Education

388

205

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“…Among computer-based learning programs, computer simulations are considered to be the most technically sophisticated option for offering various benefits for the teaching and learning of science (Blake and Scanlon 2007). A computer simulation is an interactive program that contains a model of a natural or artificial system or process (Blake and Scanlon 2007;.…”

mentioning

confidence: 99%

“…A computer simulation is an interactive program that contains a model of a natural or artificial system or process (Blake and Scanlon 2007;. It gives learners opportunities to experiment harmlessly and to simulate processes in an e-learning environment (Urhahne et al 2000).…”

mentioning

confidence: 99%

How effective is instructional support for learning with computer simulations?

et al. 2012

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The study examined the effects of two different instructional interventions as support for scientific discovery learning using computer simulations. In two well-known categories of difficulty, data interpretation and self-regulation, instructional interventions for learning with computer simulations on the topic ''ecosystem water'' were developed and tested using a sample of 124 eighth graders in science classes. The results demonstrate the effectiveness of instructional support for domain-specific factual, conceptual, and procedural knowledge acquisition. Students who received either only instructional support for data interpretation or only for self-regulation achieved the highest learning outcomes. However, a combination of instructional support for data interpretation and self-regulation seemed detrimental for knowledge acquisition. Students who received instructional interventions for both data interpretation and self-regulation also showed the highest values of perceived cognitive load. High cognitive load could be a reason for why a combination of particular instructional interventions does not lead to the expected positive learning outcomes.

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Reconsidering simulations in science education at a distance: features of effective use

Cited by 98 publications

References 20 publications

Using the Bifocal Modeling Framework to Resolve “Discrepant Events” Between Physical Experiments and Virtual Models in Biology

Using the Bifocal Modeling Framework to Resolve “Discrepant Events” Between Physical Experiments and Virtual Models in Biology

Collaborative Inquiry Learning: Models, tools, and challenges

How effective is instructional support for learning with computer simulations?

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