Chemical Education Today
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Cognitive Requirements of Open-Ended Learning Environments by William R. Robinson
Rapid advances in computer technology provide students with opportunities to engage in authentic problem solving— that is, to generate, test, and refine hypotheses; to explore and discover concepts; and to reflect on what they know and observe. Examples of such open-ended learning environments (OELEs) include Viscosity Measurement (1), which simulates a series of different techniques for measuring viscosity, GC Instrument Simulator (2), which simulates the operation of a gas chromatograph, and the well-known Lake Study (3), a two-part simulation designed to involve students with the scientific method by allowing them to collect data, formulate hypotheses, and test the hypotheses with controlled experiments. In order for learning to occur in an open-ended learning environment, users must engage in a variety of cognitive activities. These activities can demand relatively sophisticated levels of cognitive functioning for novice learners. Susan M. Land reviews these demands in her paper “Cognitive Requirements for Learning with Open-Ended Learning Environments” (4). She focuses on issues associated with three important components of technology-based OELEs (5): (i) use of tools for manipulation of visual images that facilitate experimentation with complex phenomena; (ii) exposure of the learner to authentic contexts that connect classroom knowledge and everyday experience; and (iii) presentation of a variety of OELE resources that support the learner in the inquiry process. For each of these components Land discusses the cognitive demands placed on the learner, the problems associated with these demands, and the consequent implications for design of OELEs that assist a learner in developing the skills necessary to meet the demands. Here I summarize the demands, problems, and implications that she discusses at length. We do not have space in this report to give many examples from Land’s paper; however, her manuscript contains an extensive set of references and examples. Use of Visual Manipulation Tools Effective learning from the manipulation of visual images requires a learner to generate, test, and refine theories on the basis of evidence obtained from those images. Learners must be able to recognize whether changes in a visual display have occurred as a result of manipulating one or more variables, control variables as they selectively manipulate other variables, discern which visual clues are important, draw appropriate conclusions from their observations of these cues, and relate conclusions to plausible explanations. Land reviews a variety of data that indicate novice learners have a limited ability to observe and interpret visual cues, and that their observations are often biased by inappropriate preconceptions. Problems result when learners attach mean20
ing to irrelevant cues or make observations that are biased by their preconceptions. For example, Land cites the behaviors of students using a simulation to design a virtual roller coaster (6). These students judged the speed of the coaster from looking at their video simulations, even though it was not possible to judge differences in speed from these simulations (an irrelevant cue). To keep the coaster from crashing on a curve, it needed to be slowed. Students believed that decreasing the horsepower of the engine lifting the coaster to the top of the first hill would decrease the velocity of the coaster. In fact, changing the horsepower had no effect on the velocity, but students claimed that the coaster looked as if it were slower (a preconception-based conclusion). There are a number of ways to compensate for these types of problems and to design an OELE interface so it directs the learner’s attention to key variables and cues. •
Accentuate the critical variables: for example, by highlighting them or by making a display simpler.
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Provide effective comparison of different displays using: for example, parallel display of two sets of motion in real time and again in slow motion.
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Provide explicit descriptions of the meaning of visual representations.
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Help learners focus on and interpret the significant relationships in visual representations by demonstrating these relationships or by engaging learners in discussions about them.
Use of Authentic Contexts Immersion of learners in an authentic environment, such as analysis of a series of NMR spectra or analysis of the pollution of a lake, requires them to integrate new experiences with their prior knowledge. Learners must find connections with other examples, with analogies, or with prior knowledge in order to map the events of the simulation on their prior classroom knowledge. In addition, a learner’s preconceptions may need to evolve and he or she must undergo conceptual change. Incomplete knowledge is a problem for learners. Incomplete or inaccurate prior knowledge may contradict the new ideas presented in the OELE and interfere with new learning. For example, during the design of a virtual roller coaster (6) one student recalled hearing a coaster operator say that in an emergency the coaster could be stopped at the station by using brakes and clamps. This student continually referred to the use of brakes and clamps when trying to devise ways to slow the coaster to prevent it from crashing on a curve even though brakes were not available in the simulation (prior knowledge interfering with new ideas). Learners sometimes make imprecise or unreliable observations and use these ob-
Journal of Chemical Education • Vol. 78 No. 1 January 2001 • JChemEd.chem.wisc.edu
Chemical Education Today
servations to justify naive theories. For example, Lewis and Linn (7) report that 80% of the adults they interviewed believed that objects that had been sitting in a given room were not at the same temperature because the metal objects feel cooler than other objects (an unreliable observation of temperature). To improve a learner’s ability to connect an authentic context with appropriate prior knowledge, Land suggests that OELEs should be used in a way that both prompts and guides a learner in making appropriate connections: •
Use familiar experiences and orienting strategies to prepare learners to think about concepts in ways that are familiar to them.
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Use diagrams, analogies, metaphors, or questions not only to stimulate connections to prior knowledge but also to assist the student in reorganizing that prior knowledge.
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Use a combination of technology, external questions, and collaborative dialog to guide learners as they develop their explanations.
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Engage students in conversation, thus giving instructors an opportunity to guide development.
Use of Resource-Rich Environments The programs referenced in the first paragraph of this report and many other OELEs contain a variety of help tools and sources of information. Using such programs requires metacognitive knowledge of what is known and how to fill in the gaps. (Metacognition is the active monitoring of our own thinking, knowledge, and knowledge-acquisition skills. Metacognitive knowledge results from reflecting on what we know and what we do not know, as well as how we go about learning.) Students need to identify and refine questions they ask of the environment and determine the kind of information needed from it, to evaluate the effectiveness of their searches, and to monitor the fine details of a project without losing track of its broader purpose. At the same time they must integrate information from a variety of sources. A variety of problems hinder learners in these tasks. Novice learners often lack practice monitoring their learning. Monitoring their learning is even more difficult if they are missing a base of knowledge in the domain of the OELE because inadequate knowledge hinders their evaluation and use of information resources. As Land states: Metacognition is critical to helping learners limit the search space, filter relevant from irrelevant information, and effectively coordinate questions and supporting information. Without metacognition, students can become overwhelmed in determining what information is relevant to their needs and what they need to do to refine known [search] strategies.
Novice learners often fail to refine their information gathering strategies and continue to use the same search strategies even though they know these strategies are ineffective. Many lack the background information necessary to ask
focused questions in order to narrow their search. Another problem learners face when using OELEs is their belief about the nature of teaching and learning (their epistemological orientation regarding how teaching should occur and how learning occurs). A belief that knowledge is acquired by transmission of “truth” from an instructor can lead to frustration with the construction of knowledge through exploration and may render an OELE ineffective. Managing the balance between action, information, and reflection can be difficult for learners with inadequate domain knowledge and limited experience with inquiry. Consequently it is important that an OELE provide guidance in these metacognitive activities. Suggestions for guidance include the following: •
Build into the simulation support for metacognitive activities. These activities could include, for example, questions embedded in the flow of the simulation or techniques that require learners to label their thinking.
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Point out differences between learner and expert choices.
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Design the system so strategies and progress are obvious to both the teacher and learner.
OELEs are designed to support thinking without the need for external direction. However, this does not mean that an effective OELE need not involve interactions with others. In fact, learners can benefit from such interactions. Comparing a data interpretation and the theories that develop from it with the interpretation and theories of others can assist a learner to develop the cognitive skills needed to meet the demands of all three components of a technology-based OELE. Literature Cited 1. Papadopoulos, N.; Pitta, A. T.; Markopoulos, N.; Limniou, M.; Lemos, M. A. N. D. A.; Lemos, F.; Freire, F. G. J. Chem. Educ. Software 1999, 9907. See http://JChemEd.chem.wisc.edu/ JCESoft/Programs/index.html for additional information. 2. Armitage, D. B. GC Instrument Simulator, J. Chem. Educ. Software 1999, 9901. See http://JChemEd.chem.wisc.edu/ JCESoft/Programs/index.html for additional information. 3. Whisnant, D. M.; McCormick, J. A. J. Chem. Educ. Software 1997, 5D1. See http://JChemEd.chem.wisc.edu/JCESoft/ Programs/index.html for additional information. 4. Land, S. M. Educational Technology Research and Development 2000, 48, 61–78. 5. Hannifin, M. J.; Land, S. M.; Oliver, K. In Instructional-Design Theories and Models, Volume II; Reigeluth, C., Ed.; Erlbaum: Mahwah, NJ, 1999, pp 115–140. 6. Land, S. M.; Hannifin, M. J. Educ. Technol. Res. Dev. 1997, 45, 47–73. 7. Lewis, E. L.; Linn, M. C. J. Res. Sci. Teach. 1994, 31, 657–678.
William R. Robinson is in the Department of Chemistry, Purdue University, West Lafayette, IN 47907; email:
[email protected].
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