MET:Simulation for Science Education
This page was originally authored by Joe Massie and Jennifer Long (2009)
This page was edited by Sharon Hann (2011)
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Simulations are representations of situations or processes by means of something analogous. Computer simulations represent the real world by use of a computer program. Simulations can be a valuable tool in the science classroom. They can exemplify scientific concepts and situations thereby allowing students to explore the nature of things. Issues such as cost, safety, scope, time and scale can be overcome by the use of a scientific simulation. Computer simulations help visual learners understand problems that they would not thoroughly understand simply through reading about them or solving word problems. The sophistication and variety of computer simulations in the field of science is increasing rapidly.
The History and Development of Simulations
Teachers have been using non-computer based simulations in their classrooms for many years in the form of science labs and demonstrations. Years before computers were introduced to schools, teachers tried to create activities that could show students how real life situations work, without actually being in the situation. This was necessary either because it was too expensive or dangerous to actually witness a situation (ie: radioactive decay), because the situation was impossible to witness (ie: microscopic/subatomic situations), the scale or time frame was too expansive (ie: evolution). As a result, teachers recreated and modeled these situations using hands-on materials and props. These activities are not only helpful, but essential for science teachers if they are to build an environment of learning. “The process of teaching by simply telling students about a scientific theory is viewed as inadequate, for it fails to engage the students in reflecting upon and modifying their own view of the way they think the world works.” (Richards et all, 1992)
Hands-on simulations are useful but have several drawbacks. Creating some of these activities is very labour intensive for teachers. Rode (1995) indicates that for her protein synthesis simulation “it should be noted that preparing the materials for this activity is rather tedious”. Another drawback is the necessary participation of a large number of people. In the case mentioned above, “the simulation requires the participation of a minimum of 21 students” (Rode, 1995). While group work is beneficial at times, this limits the flexibility of the simulation and could cause difficulties if the simulation takes place over several days.
Due to these and other limitations, it has been useful to incorporate computers in designing and creating simulations in science education. Computer simulations allow students to create and explore situations that they would not normally be able to witness. They can repeat trials instantly and quickly change variables to understand the effects of change. “A computer enables repeated trials of an experiment with considerable ease in a limited time, provides immediate feedback, allows simultaneous observations of graphical representations and offers a flexible environment that enables students to proceed with their own plans” (Kara, 2007).
Students are exposed to situations that would have been difficult to construct with hands-on activities. “In addition there is added variety and perhaps novelty in [computer assisted instruction] along with the potential to use vivid and animated graphics, enabling 3 dimensional aspects and other features to be viewed more realistically” (Kara and Yesilyurt, 2007). Now more than ever we have the ability to create realistic looking graphics that engage students and correspond with non-educational activities they are already doing. With a little creativity, computer based educational simulations could replace some of the potentially dangerous gaming activities students partake in.
Computer simulations have shown to increase learning. Assessment results have shown that “…instructional software programs had significantly higher effect than CG (control group)” (Kara and Yesilyurt, 2007). These results show that computer simulations increase student learning and give teachers ways to reach a new generation of learners.
How to use Simulations in the Science Classroom
Pre-instructional
Simulations introduced before the bulk of the formal teaching takes place give students a chance to think about their current understanding of the scientific topic being introduced. "Simulations based on scientific theory help to provide a set of interrelated experiences that challenge students' informal understanding of the science" (Richards et al., 1992). This can encourage genuine thought about a problem as students consider multiple potential solutions.
The use of simulation prior to any formal teaching can provide instructors with timely information about students' prior knowledge which can in turn help them to guide their formal instructional practices. According to Hargrave and Kenton, "Pre-instructional simulations can serve as a foundation for further learning, assist in the development of students' conceptions, reveal alternative conceptions in students' thinking processes, and encourage the development of questions related to content"(2000). These pre-instructional computer simulations afford students the chance to actively create their own knowledge structures, congruent with a constructivist model of learning (Papert, 2003) or a scientific discovery model of learning (de Jong & van Joolingen, 1998).
According to Hargrave and Kenton there are 4 characteristics of pre-instructional simulations that are important in science education
- they are exploratory environments File:Electric-hockey-screenshot.pngElectric Field Hockey
- they contain variables that can be manipulated by the student
- they allow the student numerous attempts to complete the task
- they provide feedback that is consistent with the phenomenon it models
- they are exploratory environments
Creating games using computer simulations can be an effective way to get students interested in learning more about a topic. For example, if the goal is for students to learn about the effects of an electric field on a charged particle, students can be given the task of creating an electric field that will put a charged particle in a net. This challenges the students to understand the concept of electric fields, but also gives them the chance to be competitive with themselves or with other students. Alternatively teachers could give the student a score based on their manipulation of variables during the simulation. Students would then have to adjust their variables to achieve a higher score and therby gain a deeper understanding of the concept and associated variables.
It should be clearly stated that students are not to be assigned simulations with no support. Podolefsky et al. (2010) concluded that scaffolding with appropriate affordances is essential to support student engagement in science simulations. This requires the teacher ensure the students are prepared with some level of understanding of the topic or basic vocabulary before embarking on the simulation exercise.
Post-instructional
Teachers can use simulations after formal instruction has been given in an attempt to keep the students from coming to incorrect conclusions or in order to use it to test the knowledge learned. Binns et al (2010) found that they could use Starry Night simulations to uncover misconceptions in understanding about moon phases and correct these with further discussion around and sing the simulation. There was a significant improvement in concept understanding by using the simulation after instruction in this manner.
Some feel that using simulations after instruction this may inhibit true scientific method as it causes students to enter the simulation activity with a narrower field of view with preconceived notions of what they are about to discover. Students need to use simulations in such a way that they can make their own discoveries and share them with their peers. "Post-instructional simulations often are used to test students' knowledge of content…" "...many post-instructional simulations do not require or encourage students to operate at advanced cognitive levels" (Hargrave and Kenton, 2000) Studies have also shown that students receiving domain information before the simulation do not profit from it (de Jong & van Joolingeng, 1998).
Post-instructional simulations can be useful for review or as a means to refer back to prior concepts needed to complete a larger picture of understanding. Teachers are encouraged to think about the learning outcomes, the prior knowledge their students have, potential mis-conceptions involved with the subject matter and the simulation they are using as factors in deciding the chronology of learning opportunities.
Supplemental Instruction
Computer simulations are a useful supplemental tool for student learning and understanding. Individuals who require more information on a topic or concept can be directed to a simulation to help further complete their knowledge building. Additionally, students with holes or gaps in their learning may find computer simulations a good way to incorporate foundational information into their understanding. Ng et al. (2009) found a significant correlation between online supplemental instruction and math final exam scores. In subject like math where foundational work is so important, gaps can be filled without the instructor needed to teach material which is pre-requisite for the course. Due to the individualized nature of the learning opportunity provided by computer simulations they are a very effective supplemental toolkit on a one to one basis.
Examples of Simulations in the Science Classroom
Biology
Chemistry
Physics
Earth Science/Geology
Agriculture
Elementary and Middle School
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1) Standardization acid moles = base moles Titration is a laboratory technique used to determine concentrations of unknown solutions. Phenolphthalein indicator turns pink when the reaction is complete, averaging volumes from multiple trials for stoichiometry.
2) Titration Curves [HA]_i=([H_3 O^+ ]_init^2)/K_a +[H_3 O^+ ]_init Drop counters perform titrations automatically, recording pH against volume of titrant added, frequently unavailable in secondary schools. pH is measured as base is added to unknown acid, or acid is added to unknown base to determine equivalence points.
3) Beer’s Law A=εLC Absorbance is proportional to path length and concentration, using spectrophotometry to scan wavelengths of maximum absorption. Limitations in linearity are caused by deviations in absorptivity coefficients at higher concentrations.
4) Boyle’s Law P=k 1/V When temperature and moles remain constant, an inverse relationship exists between pressure and volume. Although best fit is possible, graphs can be linearized as pressure against reciprocal volume.
See Also
Simulations for Medical Training
Simulation in the Humanities Classroom
Simulation for Science Dissections
Learning Math in 3D Simulations
Real World Applications of Simulations
References
Binns,I.C., Bell, R.L., and Smetana, L.K. (2010). Using technology to promote conceptual change in secondary earth science pupils' understandings of moon phases. Journal of the Research Center for Educational Technology 6(2), 112-129.
Rode, G.A., (1995). Teaching Protein Synthesis Using a Simulation, The American Biology Teacher, 57(1) 50-52.
Kara, Y. and Yesilyurt, S. (2007). Comparing the impacts of tutorial and edutainment software programs on students’ achievements, misconceptions, and attitudes towards biology. Journal of Science Education and Technology,17(1) 32-41.
Richards, J., Barowy, W., & Levin, D., (1992). Computer simulations in the science classroom. Journal of Science Education and Technology, 1(1) 67-79.
Hargrave, C. & Kenton, J. (2000). Preinstructional simulations: Implications for science classroom teaching. Journal of Computers in Mathematics and Science Teaching, 19(1) 47-58. Charlottesville, VA: AACE. (printed article)
Ng, R., Kaur, A. and Latif, L.A. (2009). Online supplemental instructions – An alternative model for the learning of mathematics. International Conference of Information, Kuala Lumpur, August 2009.
Papert, S., (2003). Mindstorms: Children, computers, and powerful ideas. In N. Waldrip-Fruin & N. Montfort (Eds.), The New Media Reader p414-431, Cambridge, MA:MIT Press
Podolefsky, N.S., Perkins, K.K. and Adams W.K., (2010). Factors promoting engaged exploration with computer simulations. Physical Review Special Topics Physics Education Research, 6(020117). [1]
de Jong, T., & van Joolingen, W.R., (1998). Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research, 68(2) 179-201. http://www.jstor.org/stable/1170753
External Links
http://phet.colorado.edu/index.php
http://puzzling.caret.cam.ac.uk/index.php?section=home&style=1
http://nobelprize.org/educational_games/
http://www.chem.iastate.edu/group/Greenbowe/sections/projectfolder/animationsindex.htm