Improving Learning in Science and Mathematics with Exploratory and Interactive Computational Modelling

Neves, Rui; Silva, Jorge; Teodoro, Vitor Duarte

doi:10.1007/978-94-007-0910-2_33

Cited by 17 publications

(12 citation statements)

References 14 publications

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“…In these questionnaires, students gave their opinion about a set of statements about the Modellus based computational modelling learning activities (Neves, Silva & Teodoro, 2011). A Likert scale starting at -3 and ending at +3 was used, where -3 stated complete disagreement and +3 complete agreement.…”

Section: Field Actions Discussion and Outlookmentioning

confidence: 99%

“…The same happens with professional scientific computation software such as Mathematica or Matlab. To avoid overloading students with the detail of programming notions and syntax, and focus the learning process on the relevant physics and mathematics, several computer modelling systems were created, for example, the Dynamical Modelling System (Ogborn, 1985), Stella (Richmond, 2004), Coach (Heck, Kadzierska & Ellermeijer, 2009), EJS (Christian & Esquembre, 2007 and Modellus (Neves, Silva & Teodoro, 2011;). …”

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Integrating Computational Modelling in Science, Technology, Engineering and Mathematics Education

Neves

Silva

Teodoro

2013

New ICMI Study Series

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Modelling is a central aspect of the research process in science, technology, engineering, and mathematics (STEM), which occurs in the cognitive context of an interactive balance between theory, experiment, and computation. The STEM learning processes should then also involve modelling in environments where there is a balanced interplay between theory, experiment, and computation. However, an adequate integration of computational themes in STEM high school and undergraduate university curricula remains to be achieved. In this chapter, we present an approach to embed computational modelling activities in the STEM learning processes which may be fruitfully adopted by curricula at secondary and introductory university levels, as well as be a valuable instrument for the professional development of teachers. To illustrate, we consider the example of physics. IntroductionScience, technology, engineering and mathematics (STEM) are evolving structures of knowledge which are symbiotically interconnected. On one hand, science is based on hypotheses and models, leading to theories, which have a strong mathematical character as scientific reasoning, concepts and laws are represented by mathematical reason-1 ing, entities and relations. On the other hand, scientific explanations and predictions must be consistent with the results of systematic and reliable experiments, which depend on technological developments as much as these depend on the progress of science and mathematics (see, e.g., Chalmers, 1999; Crump, 2001;Feynman, 1967). The creation of STEM knowledge is a dynamical cognition process which involves a blend of individual and collective actions where modelling occurs with a balance between theoretical, experimental and computational elements (Blum, Galbraith, Henn & Niss, 2007;Neunzert & Siddiqi, 2000;Schwartz, 2007; Slooten, van den Berg & Ellermeijer, 2006). In this process, computational modelling plays a key role in the expansion of the STEM cognitive horizon through enhanced calculation, exploration and visualization capabilities.Although clearly related to real world phenomena, STEM knowledge is thus built upon abstract and subtle conceptual and methodological frameworks which in addition evolve following historical dependent paths. These epistemological and cognitive features make science, technology, engineering and mathematics difficult fields to learn and to teach. An approach to STEM education that aims to be effective and in phase with the rapid scientific and technological development should then be based on pedagogical methodologies inspired in the modelling processes of STEM professional activities. Meaningful learning (see, e.g., Mintzes, Wandersee & Novak, 2005) should then occur when students go through balanced interactive explorations of the different cognitive phases associated with the modelling cycles of STEM research, starting from a qualitative contextualization phase, setting the stage for the definition, exploration, interpretation and validation of the relevant mathematical models, and ...

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Section: Field Actions Discussion and Outlookmentioning

confidence: 99%

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confidence: 99%

Integrating Computational Modelling in Science, Technology, Engineering and Mathematics Education

Neves

Silva

Teodoro

2013

New ICMI Study Series

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“…Simulasi pembentukan bayangan pada cermin cembung yang dibuat dalam artikel ini menggunakan ilmu sains komputasi yang mengkombinasikan sains, komputasi, dan matematika terapan. Ilmu komputasi merupakan bidang ilmu yang seharusnya digunakan lebih sering dalam pendidikan sains, karena menggabungkan penggunaan teknologi secara intensif dalam disiplin ilmu yang berbeda [10], serta dapat menolong siswa yang mengalami kesulitan dalam menyelesaikan persoalan Fisika [11].…”

Section: Pendahuluanunclassified

Desain Simulasi Pembentukan Bayangan Pada Cermin Cembung Menggunakan Gui Builder Scilab 5.5.0

Randjawali¹

2017

J. Penelit. Fis. Apl.

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“…The starting point to construct a mathematical model is to acknowledge that the parachutist can be represented by a point particle, located in the centre of mass, whose motion is governed by Newton's laws. Prior knowledge framing this problem involves knowledge about vectors, kinematics, constant acceleration applications of Newton's laws, comparing analytic, Euler and EulerCromer solutions [6,7], and knowledge obtained from observations of real parachute jumps. When air resistance is taken into account it is necessary to include in Newton's equations a force representing the action of the air on the motion of the particle.…”

Section: An Illustrative Example From Physicsmentioning

confidence: 99%

Improving science and mathematics education with computational modelling in interactive engagement environments

Neves¹,

Teodoro²

2012

AIP Conference Proceedings

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Abstract.A teaching approach aiming at an epistemologically balanced integration of computational modelling in science and mathematics education is presented. The approach is based on interactive engagement learning activities built around computational modelling experiments that span the range of different kinds of modelling from explorative to expressive modelling. The activities are designed to make a progressive introduction to scientific computation without requiring prior development of a working knowledge of programming, generate and foster the resolution of cognitive conflicts in the understanding of scientific and mathematical concepts and promote performative competency in the manipulation of different and complementary representations of mathematical models. The activities are supported by interactive PDF documents which explain the fundamental concepts, methods and reasoning processes using text, images and embedded movies, and include free space for multimedia enriched student modelling reports and teacher feedback. To illustrate, an example from physics implemented in the Modellus environment and tested in undergraduate university general physics and biophysics courses is discussed.

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Improving Learning in Science and Mathematics with Exploratory and Interactive Computational Modelling

Cited by 17 publications

References 14 publications

Integrating Computational Modelling in Science, Technology, Engineering and Mathematics Education

Integrating Computational Modelling in Science, Technology, Engineering and Mathematics Education

Desain Simulasi Pembentukan Bayangan Pada Cermin Cembung Menggunakan Gui Builder Scilab 5.5.0

Improving science and mathematics education with computational modelling in interactive engagement environments

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