دانلود رایگان مقاله لاتین بازی جدی در مهد کودک از سایت الزویر
عنوان فارسی مقاله:
کشف قوانین فیزیک با یک بازی جدی در مهد کودک
عنوان انگلیسی مقاله:
Discovering the laws of physics with a serious game in kindergarten
سال انتشار : 2016
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مقدمه انگلیسی مقاله:
1. Introduction
Scientific thinking has become more relevant in recent years. Many countries aim to stimulate scientific thinking via science education in order train future researchers, but also to make children think critically (Osborne, 2013). Serious games may very well be a supportive tool in this respect (Hamari, Koivisto, & Sarsa, 2014; Morris, Croker, Zimmerman, Gill, & Romig, 2013), and they also allow the assessment of how children play them. As a case in point, a physics game may help identify exactly how young children discover scientific concepts. In kindergarten, there is already some knowledge of physics (Inhelder & Piaget, 1958), but little is known about how children gain this knowledge, and how individual differences can be explained. In a game-like assessment, children would require explorative and efficient behavior. Exploration is about where to look, while efficiency is about understanding what is seen (Klahr & Dunbar, 1988). Both exploration and efficiency have beenshown to foster scientific thinking, but which factors underlie these effects is largely unknown, especially in young children (see Legare, 2014 for a review). To identify the basis of exploration and efficiency, both cognitive (executive control, reasoning ability), and linguistic (vocabulary) factors may play a role (Wagensveld, Kleemans, Segers, & Verhoeven, 2015). In the present study, we therefore assessed individual differences in exploration and efficiency during physics gaming in kindergarten. 1.1. Serious games to enhance scientific thinking To participate in our contemporary knowledge-based society, a broad set of skills and abilities are required, such as scientific thinking (Dede, 2010). Scientific thinking consists of scientific reasoning, i.e. domain-general skills, and scientific knowledge, i.e. domain-specific knowledge (Klahr, Zimmerman, & Jirout, 2011). Scientific reasoning is the intentional seeking of knowledge using the scientific method (Kuhn, 2004). The three components of scientific reasoning are: hypothesis generation, designing and conducting experiments, and evaluating the evidence (Klahr, 2000). These three components allow people to evaluate the validity of science-related claims, and to generate new knowledge (Fischer et al., 2014), which helps them to become critical and autonomic thinkers. To support the development of these skills, scientific activities are being undertaken in schools (e.g., Lorch, Lorch, Freer, Dunlap, & Hodell, 2014). Children can acquire scientific knowledge about specific domains via these scientific activities. An example is learning why objects float or sink (Hardy, Jonen, Moller, € & Stern, 2006). Children can acquire this domain-specific knowledge via exploration and efficiency (Klahr & Dunbar, 1988). Via exploration, children can identify what the factors are that determine the effect, such as whether a boat will float. Via efficient behavior, children can generate a desired effect, such as designing a boat that will float. These types of behavior underlie scientific thinking (Legare, 2014). A recent approach to scientific thinking in education is the use of serious games. Games provide a rich learning environment and allow children to select their own choices, which strengthens their motivation (Morris et al., 2013). The richness of serious games motivates children to process multiple, possibly interacting variables, which makes them especially suitable for science education (Morris et al., 2013). Just as rule discovery and induction are inherent to scientific thinking (Dunbar & Klahr, 2012), so are they to gaming (Greenfield et al., 1994). The richness of serious games also provides an opportunity for exploration, as there is more vigorous and prolonged exploration of stimuli that can be described as complex (Berlyne, 1966). Another advantage of serious games is that they can be regarded as ongoing assessments, providing players with the opportunity to show what they have learned (Morris et al., 2013). Learning is not linear, but multiple strategies coexist to solve a problem, which is affected by experience (Siegler, 2000), which seems to be the case in serious games as well. Therefore, serious games seem an ideal tool to assess children’s explorative behavior and problem-solving skills within science education.
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کلمات کلیدی:
Physical law - Wikipedia https://en.wikipedia.org/wiki/Physical_law A physical law or scientific law is a theoretical statement "inferred from particular facts, applicable to a defined group or class of phenomena, and expressible by the statement that a particular phenomenon always occurs if certain conditions be present." Physical laws are typically conclusions based on repeated scientific ... Algorithm discovers physical laws - physicsworld.com physicsworld.com/cws/article/news/2009/apr/02/algorithm-discovers-physical-laws Apr 2, 2009 - Physicists show how scientific discovery can be partially automated. ... algorithm that can deduce physical laws from raw experimental data with ... Discovering the laws of urbanisation https://arxiv.org › physics by F Simini - 2015 - Related articles Dec 11, 2015 - arXiv.org > physics > arXiv:1512.03747 ... Physics > Physics and Society ... Here we present two fundamental laws of urbanisation that are ... How do scientists discover laws? - Quora https://www.quora.com/How-do-scientists-discover-laws I would say, first step: you observe the world around you. Maybe for a long long time. Second ... Shilpa Bhandari, Why bio when you love physics? Answered Oct ... Chess and the laws of physics | ChessBase en.chessbase.com/post/che-and-the-laws-of-physics Apr 16, 2006 - Richard Feynman once noted that discovering the laws of physics is like trying to learn the laws of chess merely by observing chess games. Searches related to Discovering the laws of physics distilling free-form natural laws from experimental data eureka machine eureqa cornell michael schmidt cornell eureqa download eureka double pendulum eureqa machine learning eureqa nutonian
