the modelling tool, pupils may construct a new model from their mental model of the
realistic phenomenon and validate the model by comparing modelling outcomes
with experimental results. Patterns for teachers to prepare a lesson, using the
modelling tool are similar; the teacher might use a ready model, modify it a bit or
develop a new model.
Second, the software allows importing measured data and graphs to the modelling
activity. This enables simultaneous observations of the modelling graph (i.e. an
outcome from the theoretical world) and the experimental graph (i.e. an outcome
from the physical world) in the same diagram (Fig. 12.5). It is convenient for pupils
to compare these outcomes of the two worlds. If the modelling result does not fit the
real data, then pupils can adjust the model (e.g. changing parameters, adding variables, correcting relationships), execute it again, and compare new modelling results
with the real data. The modelling tool enhances opportunities for many rounds of
thinking back and forth between the theoretical and physical worlds.
Last but not least, the incorporation of the modelling tool enables pupils to (a) get
used to modelling as a scientific tool in computational science (doing science with
computer), (b) appreciate what modelling is as a way of thinking, (c) understand how
important it is in science, and (d) develop a critical attitude by working with several
models for one and the same phenomenon (i.e. modelling cycle). In their article,
submitted in November 2008, Heck and Ellermeijer (2009) used Coach video
measurements and models of runners to predict the possible time: 9.6 s for 100 m
of Usain Bolt based on his Olympic run in Beijing 2008. This model accurately
predicted or apparently affirmed his world record at the 2009 World Championships
in Athletics in Berlin (9.58 s). This instance illustrates the power of the Coach tools
in explaining and predicting real-world phenomena like sprinter’s run. Additionally,
Heck and colleagues showcased students’ research projects (i.e. yoyos, alcohol
metabolism, beer foam, bouncing balls) in which pupils could build models from
simple to more complex (i.e. progressive modelling approach) by incorporating
more factors aimed at better matching between the model and reality (Heck 2007,
2009; Heck et al. 2009a).
12.4.4 Data Processing and Analysis in Coach: Generic
Components of the Coach Tools
Generated from the model or collected from the experiment or the video, numerical
data can be then quickly transformed into more comprehensible, graphical representations: graphs, tables and animations. If such representation forms are arranged in
advance, then pupils can see, for example, how empirical graphs appear (i.e. realtime graphing) or how animated objects move during the measurement or generation
of data.
Additionally, just requiring simple manipulations, the software provides pupils
with many possibilities for elementary analysis such as scan, slope, area and further
12 Stem, Inquiry Practices and Technology in Physics Education
141
realistic phenomenon and validate the model by comparing modelling outcomes
with experimental results. Patterns for teachers to prepare a lesson, using the
modelling tool are similar; the teacher might use a ready model, modify it a bit or
develop a new model.
Second, the software allows importing measured data and graphs to the modelling
activity. This enables simultaneous observations of the modelling graph (i.e. an
outcome from the theoretical world) and the experimental graph (i.e. an outcome
from the physical world) in the same diagram (Fig. 12.5). It is convenient for pupils
to compare these outcomes of the two worlds. If the modelling result does not fit the
real data, then pupils can adjust the model (e.g. changing parameters, adding variables, correcting relationships), execute it again, and compare new modelling results
with the real data. The modelling tool enhances opportunities for many rounds of
thinking back and forth between the theoretical and physical worlds.
Last but not least, the incorporation of the modelling tool enables pupils to (a) get
used to modelling as a scientific tool in computational science (doing science with
computer), (b) appreciate what modelling is as a way of thinking, (c) understand how
important it is in science, and (d) develop a critical attitude by working with several
models for one and the same phenomenon (i.e. modelling cycle). In their article,
submitted in November 2008, Heck and Ellermeijer (2009) used Coach video
measurements and models of runners to predict the possible time: 9.6 s for 100 m
of Usain Bolt based on his Olympic run in Beijing 2008. This model accurately
predicted or apparently affirmed his world record at the 2009 World Championships
in Athletics in Berlin (9.58 s). This instance illustrates the power of the Coach tools
in explaining and predicting real-world phenomena like sprinter’s run. Additionally,
Heck and colleagues showcased students’ research projects (i.e. yoyos, alcohol
metabolism, beer foam, bouncing balls) in which pupils could build models from
simple to more complex (i.e. progressive modelling approach) by incorporating
more factors aimed at better matching between the model and reality (Heck 2007,
2009; Heck et al. 2009a).
12.4.4 Data Processing and Analysis in Coach: Generic
Components of the Coach Tools
Generated from the model or collected from the experiment or the video, numerical
data can be then quickly transformed into more comprehensible, graphical representations: graphs, tables and animations. If such representation forms are arranged in
advance, then pupils can see, for example, how empirical graphs appear (i.e. realtime graphing) or how animated objects move during the measurement or generation
of data.
Additionally, just requiring simple manipulations, the software provides pupils
with many possibilities for elementary analysis such as scan, slope, area and further
12 Stem, Inquiry Practices and Technology in Physics Education
141