Wolff-Michael Roth shows how computer simulations can be used to help pupils see science demonstrations in ways that support their learning
FOR NEARLY 40 YEARS, computers and associated technologies have been touted as the panacea for all educational problems. In the 1970s, they were thought to be able to replace teachers; in the 1980s, intelligent tutors and computer-aided data collection were to allow pupils to learn maths and science; and the advent of the internet in the 1990s was to provide pupils with all the “information” that they need to learn. But in the teaching and learning of science, research does not bear out all the hype.
|What we know|
|● Computing technology is a context in which pupils and teachers can talk – and, more importantly, listen – to each other.
● For science pupils, learning does not mean taking in information, because they are not yet tuned in a way to decode what experiments, simulations, teachers, or textbooks are sending.
● Research on language in science classrooms shows that interactions with the teacher are crucial in tuning pupils so that they can receive the information from textbooks, internet, and lectures.
Beliefs shape what scientists see
It is widely established that our current theories shape what we see. Studies in scientific laboratories have shown that even experienced and successful research scientists tend to see new data through the lenses of their existing theories/beliefs. Scientists only begin to question their own thinking over time, when the new data consistently do not fit the existing scientific canon; and sometimes scientists learn from their graduate pupils that they have to look differently to understand that a new theory is required. That is, they do not change their theories/beliefs on the basis of data alone.
Beliefs shape what pupils see
Controlled studies show that secondary school pupils see scientific demonstrations in ways that differ from what their teachers believe they see. Thus, in one study, a class of sixth form physics pupils was asked to note their prediction about the outcome of a demonstration involving a person standing on a rotating table. They then observed and recorded what they had seen in the experiment. Of the 23 pupils in the class, 18 pupils noted that they had seen the person on the table move and 5 had seen no motion. In all instances this was consistent with the predictions they had made. Moreover, pupils provided explanations for their observations, so that there was no doubt about what they had seen and how this fitted their theory/beliefs. How can it be that, looking at the same science demonstration clearly visible from all parts of the classroom, pupils saw very different phenomena? In fact, in similar conditions, the teacher provided his explanation for the outcome of a demonstration, but the research showed that the pupils had not observed what the teacher believed everybody to have seen. Consequently, the pupils developed wrong explanations: they were fitting what the teacher said to their own observation, which, in many instances, was opposite to what the teacher believed they had seen (motion vs. no motion).
It is precisely here that computer technology has proven to be beneficial, though not in the sense that one initially might think. A research study conducted in my own college preparatory physics classroom showed that the pupils, while using computer simulations of Newtonian physics, did not see what they needed to see to learn the physics that the program was designed to teach them. For example, the video showing a pupil group working with the simulation clearly shows how objects were moving upward on the computer screen before “falling” downward; and yet the pupils did not see the initial upward movement. This happened, even though there were arrows attached to the objects that initially pointed upward, then downward together with the associated movement.
Teaching for seeing
Helping pupils to see what they do not readily see is precisely where computing technology can assist, because simulations can be shown repeatedly, in slow or rapid motion, and so on. The episode from a real physics classroom, shown in Figure 1, shows how the teacher first asks pupils to predict what will happen if they run the simulation depicted on the monitor. Having conducted such a simulation before, the pupils initially suggest that the object would go down. The teacher acknowledges the responses of Ryan and Glen, and then runs the simulation, but he does so in slow motion. Elizabeth begins to say something, then stops and comments, “it went backwards though”. There is a very rapid input from all three pupils, and then they settle the issue: The object “went upwards”, “backwards first”, and the “way the little arrow is”, before it moved downward. To understand the relation between the velocity of the object (thin arrow) and the force (fat arrow) acting upon them, pupils need to see the object move up and slow down (also indicated by the ever shortening thin arrow), before the effect of the downward force reverses its velocity.
In this episode, taken from a research study, we see how pupils learn to see the events in the scientific way as a result of their interaction with the teacher. The latter has previously listened in on the conversation between the three pupils, and he has realised that they were not seeing what they needed to see to understand the physics of motion. In the episode, he decides to get involved without actually lecturing them about physics, but by providing an appropriate opportunity for them to learn how to look and see. The teacher uses the slow motion feature and a very specific orientation of the two arrows (force, velocity), which facilitates concentration on the important part: the object moves upward before moving down and the velocity arrow gets shorter and reverses its direction and gets longer again. This is precisely what they need to see to understand, and yet had not seen while using the simulation on their own.
Talking science . . .
The upshot of such research is that computers and related technology should not be thought of as information providers – pupils are likely to see and understand in ways that are not consistent with science. Rather, we can think of computing technology as a context in which pupils and teachers can talk – and, more importantly, listen – to each other. In the process, science pupils learn to see and understand just what the real information is; and science teachers learn to see and hear what pupils know, perceive, and believe. Science teachers thereby learn that what they think is information given to pupils is actually not perceived and understood as such.
. . . is tuning into science
Engineers have known for a long time that a sender and a receiver have to be tuned in the same way so that the intended information is decoded and understood as such. For science pupils, learning does not mean taking in information, because they are not yet tuned in a way to decode what experiments, simulations, teachers, or textbooks are sending. The conversations pupils have with each other over and about computer simulation is part of this mutual tuning process. Research on language in science classrooms shows that interactions with the teacher are crucial elements in tuning pupils so that they can receive the information from textbooks, internet, and lectures.
About the author
Wolff-Michael Roth is Lansdowne Professor of Applied Cognitive Science at the University of Victoria in Canada. Having published more than 300 research articles and more than 35 books, he is the recipient of many awards for his continued contributions to, leadership in, and substantial impact on science education through research.
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Roth W-M (2005), Talking Science: Language and Learning in Science Classrooms. Lanham, MD: Rowman & Littlefield.
Roth W-M (2009), Radical Uncertainty in Scientific Discovery Work. Science, Technology & Human Values, 34, 313–336.
Roth W-M, McRobbie C, Lucas KB, & Boutonné S (1997), Why do Students Fail to Learn from Demonstrations? A Social Practice Perspective on Learning in Physics. Journal of Research in Science Teaching, 34, 509–533.
Roth W-M (2008), Emergence of Analogies in Collaboratively Conducted Computer Simulations. In R. Zheng (Ed.), Cognitive Effects of Multimedia Learning, pp. 340–361. Hershey, PA: IGI Global.
Yerrick R, & Roth W-M (Eds.) (2005), Establishing Scientific Classroom Discourse Communities: Multiple Voices of Research on Teaching and Learning. Mahwah, NJ: Lawrence Erlbaum Associates.