Chemical Education Today

20 Journal of Chemical Education • Vol. 78 No. 1 January 2001 • JChemEd.chem.wisc.edu

Reports from Other Journals

Cognitive Requirements of Open-Ended LearningEnvironmentsby William R. Robinson

Rapid advances in computer technology provide studentswith opportunities to engage in authentic problem solving—that is, to generate, test, and refine hypotheses; to exploreand discover concepts; and to reflect on what they know andobserve. Examples of such open-ended learning environments(OELEs) include Viscosity Measurement (1), which simulatesa series of different techniques for measuring viscosity, GCInstrument Simulator (2), which simulates the operation of agas chromatograph, and the well-known Lake Study (3), atwo-part simulation designed to involve students with thescientific method by allowing them to collect data, formu-late hypotheses, and test the hypotheses with controlled ex-periments.

In order for learning to occur in an open-ended learn-ing environment, users must engage in a variety of cognitiveactivities. These activities can demand relatively sophisticatedlevels of cognitive functioning for novice learners. Susan M.Land reviews these demands in her paper “Cognitive Require-ments for Learning with Open-Ended Learning Environ-ments” (4). She focuses on issues associated with three im-portant components of technology-based OELEs (5): (i) useof tools for manipulation of visual images that facilitate ex-perimentation with complex phenomena; (ii) exposure of thelearner to authentic contexts that connect classroom knowl-edge and everyday experience; and (iii) presentation of a va-riety of OELE resources that support the learner in the in-quiry process. For each of these components Land discussesthe cognitive demands placed on the learner, the problemsassociated with these demands, and the consequent implica-tions for design of OELEs that assist a learner in developingthe skills necessary to meet the demands. Here I summarizethe demands, problems, and implications that she discussesat length. We do not have space in this report to give manyexamples from Land’s paper; however, her manuscript con-tains an extensive set of references and examples.

Use of Visual Manipulation Tools

Effective learning from the manipulation of visual im-ages requires a learner to generate, test, and refine theorieson the basis of evidence obtained from those images. Learn-ers must be able to recognize whether changes in a visual dis-play have occurred as a result of manipulating one or morevariables, control variables as they selectively manipulate othervariables, discern which visual clues are important, draw ap-propriate conclusions from their observations of these cues,and relate conclusions to plausible explanations.

Land reviews a variety of data that indicate novice learn-ers have a limited ability to observe and interpret visual cues,and that their observations are often biased by inappropriatepreconceptions. Problems result when learners attach mean-

ing to irrelevant cues or make observations that are biasedby their preconceptions. For example, Land cites the behav-iors of students using a simulation to design a virtual rollercoaster (6). These students judged the speed of the coasterfrom looking at their video simulations, even though it wasnot possible to judge differences in speed from these simula-tions (an irrelevant cue). To keep the coaster from crashingon a curve, it needed to be slowed. Students believed thatdecreasing the horsepower of the engine lifting the coasterto the top of the first hill would decrease the velocity of thecoaster. In fact, changing the horsepower had no effect onthe velocity, but students claimed that the coaster looked asif it were slower (a preconception-based conclusion).

There are a number of ways to compensate for thesetypes of problems and to design an OELE interface so it di-rects the learner’s attention to key variables and cues.

• Accentuate the critical variables: for example, by high-lighting them or by making a display simpler.

• Provide effective comparison of different displays us-ing: for example, parallel display of two sets of mo-tion in real time and again in slow motion.

• Provide explicit descriptions of the meaning of visualrepresentations.

• Help learners focus on and interpret the significantrelationships in visual representations by demonstrat-ing these relationships or by engaging learners in dis-cussions about them.

Use of Authentic ContextsImmersion of learners in an authentic environment, such

as analysis of a series of NMR spectra or analysis of the pol-lution of a lake, requires them to integrate new experienceswith their prior knowledge. Learners must find connectionswith other examples, with analogies, or with prior knowl-edge in order to map the events of the simulation on theirprior classroom knowledge. In addition, a learner’s precon-ceptions may need to evolve and he or she must undergo con-ceptual change.

Incomplete knowledge is a problem for learners. Incom-plete or inaccurate prior knowledge may contradict the newideas presented in the OELE and interfere with new learn-ing. For example, during the design of a virtual roller coaster(6) one student recalled hearing a coaster operator say thatin an emergency the coaster could be stopped at the stationby using brakes and clamps. This student continually referredto the use of brakes and clamps when trying to devise waysto slow the coaster to prevent it from crashing on a curveeven though brakes were not available in the simulation (priorknowledge interfering with new ideas). Learners sometimesmake imprecise or unreliable observations and use these ob-

Chemical Education Today

JChemEd.chem.wisc.edu • Vol. 78 No. 1 January 2001 • Journal of Chemical Education 21

servations to justify naive theories. For example, Lewis andLinn (7) report that 80% of the adults they interviewed be-lieved that objects that had been sitting in a given room werenot at the same temperature because the metal objects feelcooler than other objects (an unreliable observation of tem-perature).

To improve a learner’s ability to connect an authenticcontext with appropriate prior knowledge, Land suggests thatOELEs should be used in a way that both prompts and guidesa learner in making appropriate connections:

• Use familiar experiences and orienting strategies toprepare learners to think about concepts in ways thatare familiar to them.

• Use diagrams, analogies, metaphors, or questions notonly to stimulate connections to prior knowledge butalso to assist the student in reorganizing that priorknowledge.

• Use a combination of technology, external questions,and collaborative dialog to guide learners as they de-velop their explanations.

• Engage students in conversation, thus giving instruc-tors an opportunity to guide development.

Use of Resource-Rich EnvironmentsThe programs referenced in the first paragraph of this

report and many other OELEs contain a variety of help toolsand sources of information. Using such programs requiresmetacognitive knowledge of what is known and how to fillin the gaps. (Metacognition is the active monitoring of ourown thinking, knowledge, and knowledge-acquisition skills.Metacognitive knowledge results from reflecting on what weknow and what we do not know, as well as how we go aboutlearning.) Students need to identify and refine questions theyask of the environment and determine the kind of informa-tion needed from it, to evaluate the effectiveness of theirsearches, and to monitor the fine details of a project withoutlosing track of its broader purpose. At the same time theymust integrate information from a variety of sources.

A variety of problems hinder learners in these tasks. Nov-ice learners often lack practice monitoring their learning.Monitoring their learning is even more difficult if they aremissing a base of knowledge in the domain of the OELE be-cause inadequate knowledge hinders their evaluation and useof information resources. As Land states:

Metacognition is critical to helping learners limit thesearch space, filter relevant from irrelevant information,and effectively coordinate questions and supporting in-formation. Without metacognition, students can becomeoverwhelmed in determining what information is relevantto their needs and what they need to do to refine known[search] strategies.

Novice learners often fail to refine their informationgathering strategies and continue to use the same search strat-egies even though they know these strategies are ineffective.Many lack the background information necessary to ask

focused questions in order to narrow their search. Anotherproblem learners face when using OELEs is their belief aboutthe nature of teaching and learning (their epistemological ori-entation regarding how teaching should occur and how learn-ing occurs). A belief that knowledge is acquired by transmis-sion of “truth” from an instructor can lead to frustration withthe construction of knowledge through exploration and mayrender an OELE ineffective.

Managing the balance between action, information, andreflection can be difficult for learners with inadequate do-main knowledge and limited experience with inquiry. Con-sequently it is important that an OELE provide guidance inthese metacognitive activities. Suggestions for guidance in-clude the following:

• Build into the simulation support for metacognitiveactivities. These activities could include, for example,questions embedded in the flow of the simulation ortechniques that require learners to label their thinking.

• Point out differences between learner and expertchoices.

• Design the system so strategies and progress are obvi-ous to both the teacher and learner.

OELEs are designed to support thinking without theneed for external direction. However, this does not mean thatan effective OELE need not involve interactions with oth-ers. In fact, learners can benefit from such interactions. Com-paring a data interpretation and the theories that developfrom it with the interpretation and theories of others can as-sist a learner to develop the cognitive skills needed to meetthe demands of all three components of a technology-basedOELE.

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. Software1997, 5D1. See http://JChemEd.chem.wisc.edu/JCESoft/Programs/index.html for additional information.

4. Land, S. M. Educational Technology Research and Development2000, 48, 61–78.

5. Hannifin, M. J.; Land, S. M.; Oliver, K. In Instructional-De-sign 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:wrrobin@purdue.edu.