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A View of the Science Education Research Literature: Scientific Discovery Learning with Computer Simulations by William R. Robinson
In their study, “Scientific Discovery Learning with Computer Simulations of Concept Domains”, Ton de Jong and Wouter van Joolingen (1 ) review research that addresses the effectiveness of simulations in promoting scientific discovery learning and the problems that learners may encounter as they use discovery learning. This review is important not only for the guidance it provides to designing simulations but also for what it says about students’ ability to reason scientifically in general. Initially, discovery learning was focused on discovering concepts (2 ). However, more recent use of the technique has moved towards scientific discovery learning. The review reminds us that: “In scientific discovery learning the main task of the learner is to infer the characteristics of the model underlying the simulation. The learners’ basic actions are changing values of input variables and observing the resulting changes in values of output variables.”
Analyses of scientific discovery learning are usually based on models of discovery as practiced by scientists. One such model involves six components (3): define the problem, state a hypothesis, design an experiment, collect, analyze, and interpret data, apply the results, and make predictions based on the results
This model not only fits students engaged in scientific discovery learning through simulations but also fits chemistry students engaged in other hands-on exploratory activities, including discovery laboratories, project laboratories, and research. The problems students face in these real laboratory activities are similar to the problems they face in scientific discovery learning through simulations. In their review de Jong and van Joolingen describe four categories of problems students face in discovery learning: hypothesis generation, experimental design, data interpretation, and regulation of the discovery process. Hypothesis Generation There are several reasons students have difficulty generating hypotheses appropriate to the problem at hand. Many students do not know what a hypothesis should look like; their hypotheses do not contain variables or the relations between variables and data. A second problem is that many stu-
dents do not use their data to revise their hypotheses. These students retain a hypothesis on the basis of a negative experimental result. Alternatively, they may ignore anomalous data, a reflection of the fact that people have a strong tendency to keep their original ideas. Finally, learners tend to avoid hypotheses in which the relations among variables have a high level of precision because such hypotheses are subject to ready rejection by experimental data. For example, the hypothesis that a gas expands when heated may be preferred over the hypothesis that the volume of a gas is directly proportional to its temperature on the kelvin scale. Experimental Design Many learners try poorly designed experiments. Several studies have shown that a natural bias toward confirmation leads learners to devise experiments designed to provide information that confirms a hypothesis instead of disconfirming it. Other studies show that students design inclusive experiments; for example, they may vary too many variables in one experiment. On the other hand, some students do not use the whole range of potentially informative experiments that are available. Other students construct experiments that are not intended to test a hypothesis; they use experiments that attempt to create some desirable outcome rather than to test their model. Data Interpretation Successful learners are more proficient at finding regularities in the data than unsuccessful learners. Errors in misinterpretation of data are common and, interestingly, misinterpretation that results in confirmation of a current hypothesis is the most common error. Interpretation of graphs is also a difficult process for many students. Regulation of the Discovery Process Organization, planning, and systematic experimentation are characteristics of learners who engage successfully in the discovery learning process. It has been observed that successful learners plan experiments and pay close attention to data management. Less successful students use a more random strategy and concentrate on more immediate, short-term decisions rather than following a more extended plan. Setting the goal to be achieved is a problem for learners with little prior knowledge of the domain of the problem. (The domain of a problem includes the background, concepts, mathematical and conceptual manipulations, and experimental methods required by the problem.)
JChemEd.chem.wisc.edu • Vol. 77 No. 1 January 2000 • Journal of Chemical Education
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Chemical Education Today
Other Observations Many of the papers that identify problems with a simulation also report ways to provide support for learners so they can make effective use of the simulation. Several of the methods suggested (1) were used in combination with other supports, and their individual effects could not be determined, but they may be promising. Research results indicate three other methods of support seem to influence learning outcomes in a positive way: 1. Providing direct, concurrent access to information about the domain of the simulation appears helpful. The information should be presented concurrently with the simulation so that it is available at the appropriate time. 2. Providing learners with assignments, questions, exercises, or games has a positive effect. These provide the learner with a goal. 3. Learners who use simulations that introduce the components of the simulation gradually are more successful than learners who use a simulation that introduces the full complexity of the simulation at once.
de Jong and van Joolingen also consider the instructional goals for scientific discovery learning. They conclude that “ad-
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vantages of simulations seem clear when the instructional goal is mastery of discovery skills.” Discovery skills include reasoning from hypotheses, applying a systematic and planned discovery process, and the use of high-quality experimental design. The authors cite frequent claims that scientific discovery learning results in knowledge that is more intuitive and deeply rooted in a learner’s knowledge base and is more qualitative in nature than knowledge gained by more traditional exercises. However, they warn that the results of simulationbased learning can be properly measured only by assessments that measure implicit application of the knowledge rather than recognition and understanding or explicit application. Literature Cited 1. de Jong, T.; van Joolingen, W. R. Rev. Educ. Res. 1998, 68, 179–201. 2. Bruner, J. S. Harvard Educ. Rev. 1961, 31, 21–32. 3. Friedler, Y.; Nachmias, R; Lynn, M. C. J. Res. Sci. Teach. 1990, 27, 173–191.
William R. Robinson is in the Department of Chemistry, Purdue University, West Lafayette, IN 47907;
[email protected].
Journal of Chemical Education • Vol. 77 No. 1 January 2000 • JChemEd.chem.wisc.edu
