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* Wilensky, U., & Stroup,W. (2002, April). Participatory simulations: Envisioning the networked classroom as a way to support systems learning for all. Paper presented at the annual meeting of the American Educational Research Association, New Orleans, LA.
* Wilensky, U., & Stroup,W. (2002, April). Participatory simulations: Envisioning the networked classroom as a way to support systems learning for all. Paper presented at the annual meeting of the American Educational Research Association, New Orleans, LA.
[[Category:Edutech families]]

Revision as of 18:16, 9 July 2009

This article is in a large part a synthesis of Rieber 1996

Definitions

  • Microworlds are small playground of the mind (Clements, 1989, p. 86 cited by Rieber, 1996:587),
  • Microworlds are tiny worlds inside which a student can explore alternatives, test hypotheses, and discover facts that are true about that world. (Larry Latour).
  • A microworld implements a constructivist instructional design model that lets the learner "play" within an artificial or real (e.g. a Sandbox) environment and learn by building things. The purpose is to give students the resources to build and refine their own knowledge.
  • “A microworld is a type of computational document aimed at embedding important ideas in a form that students can readily explore. The best microworlds have an easy-to-understand set of operations that students can use to engage tasks of value to them, and in doing so, they come to understanding powerful underlying principles. You might come to understand ecology, for example, by building your own little creatures that compete with and are dependent on each other.” (diSessa, 2000, p.47)

History

In 1980, Papert made popular concepts developped around the programming language Logo whose design was influenced by a particular constructionist vision of education. Logo included "turtle geometry", a drawing pen in the form of a turtle that children could move and draw around on the screen or the floor. The turtle is an "object to think with", i.e. a cognitive tool. Since Logo many other environments in the same spirit have been built, but the scope of modern micro-worlds goes much beyound programming environments for children.

Papert (1980) gave a first formal definition of a microworld as a:“...subset of reality or a constructed reality whose structure matches that of a given cognitive mechanism so as to provide an environment where the latter can operate effectively. The concept leads to the project of inventing microworlds so structured as to allow a human learner to exercise particular powerful ideas or intellectual skills.”(p. 204)

For Papert, a microworld is based to a large degree on the way in which an individual is able to use a technological tool for the kinds of thinking and cognitive exploration that would not be possible without the technology.

But Papert knew that a learner needs support structures: “...The use of the microworld provides a model of a learning theory in which active learning consists of exploration by the learner of a microworld sufficiently bounded and transparent for constructive exploration and yet sufficiently rich for significant discovery.”(p. 208)

While it demonstrates the importance Papert placed on exploration and discovery learning, it also shows the need for a teacher or a microworld designer to identify boundaries for learning. Papert has maybe underestimated the difficulty of designing such boundaries, especially identifying where the boundaries lie for a particular child in a particular domain, but he certainly recognized the need for guidance, both in the microworld itself and in the teacher’s assistance to a child using it. As Papert (1980) writes,“...The construction of a network of microworlds provides a vision of education planning that is in important respects “opposite” to the concept of “curriculum.” This does not mean that no teaching is necessary or that there are no “behavioral objectives.” But the relationship of the teacher to learner is very different: the teacher introduces the learner to the microworld in which discoveries will be made, rather than to the discovery itself.”(p. 209)

Initially LOGO-like environments, were confined to the computer and the individual users. More recent developments (e.g. in MIT's Lifelong Kindergarten group) concern a wide range of constructionist learning objects such as:

Features of microworlds

A microworld would consist of (Edwards, 1995)
  • A set of computational objects that model the mathematical or physical properties of the microworld’s domain
  • Links to multiple representations of the underlying properties of the model
  • The ability to combine objects or operations in complex ways, similar to the idea of combining words and sentences in a language
  • A set of activities or challenges that are inherent or preprogrammed in the microworld; the student is challenged to solve problems, reach a goal, etc.
So microworlds
  • allow more and younger people to understand highly significant and applicable concepts and principles underlying all complex systems.
  • are objects to think with to extend our intellectual capabilities. the Logo turtle is an example: the turtle becomes a way for the child to grapple with mathematical ideas usually considered too difficult or abstract. A prime role served by the turtle is the way it “concretizes” abstract ideas.
  • are central to some forms of exploratory learning.
  • have the idea of debugging: errors in problem-solving tasks become a rich source of information, without which a correct solution could not be found.
A software can be considered as a microworld...or not

Whether or not the software can be considered a microworld depends on this interrelationship when the software is actually used. Student have to:

  • understand a simple aspect of the domain very quickly (Rieber, 1996b);
  • explore the domain further with the microworld (Rieber, 1996b);
  • be able to manipulate the objects and features of the microworld “with the purpose of inducing or discovering their properties and the functioning of the system as a whole” (Edwards, 1995, p. 144).
  • be able to interpret the feedback generated by the software based on their actions and modify the microworld to achieve their goal (i.e., debugging).
  • “use the objects and operations in the microworld either to create new entities or to solve specific problems or challenges (or both)” (Edwards, 1995, p. 144).

Therefore, a microworld must be defined at the interface between an individual user in a social context and a software tool possessing the following five functional attributes:

  • domain specific;
  • provides a doorway to the domain for the user by offering a simple example of the domain that is immediately understandable by the user;
  • leads to activity that can be intrinsically motivating to the user—the user wants to participate and persist at the task for some time;
  • leads to immersive activity best characterized by words such as play, inquiry, and invention; and
  • situated in a constructivist philosophy of learning (demands a very able teacher serving a dual role: teacher-asfacilitator and teacher-as-learner).
Simulations are not necessarily microworlds

Microworlds have two important characteristics that may not be present in a simulation (Rieber, 1996).

  1. a microworld presents the learner with the "simplest case" of the domain, even though the learner would usually be given the means to reshape the microworld to explore increasingly more sophisticated and complex ideas.
  2. a microworld must match the learner's cognitive and affective state. Learners immediately know what to do with a microworld - little or no training is necessary to begin using it (imagine first "training" a child how to use a sandbox).

the student is encouraged to think about it as a "real" world, and not simply as a simulation of another world. (http://www.umcs.maine.edu/~larry/microworlds/microworld.html)

Alternative names and/or specialized kinds (see Rieber, 1996, p. 583)

Theorical basis for learning in a microworld

Representations assist problem solving activities in 3 ways:

  1. the right representation reduces the cognitive load and allows students to use their precious working memory for higher-order tasks.
  2. representations clarify the problem space for students, such as by organizing the problem and the search path.
  3. a good representation reveals immediate implications.

Microworlds offer the means of maximizing all 3 benefits of representations, when used in the context of an appropriate science teaching pedagogy, such as one based on the scientific method of hypothesis generating and hypothesis testing. For example, in the ThinkerTools microworld, students directly interact with a dynamic object while having the discrete forces they impart on the object horizontally or vertically displayed on a simple, yet effective datacross. Students can also manipulate various parameters in the microworld, such as gravity and friction. ThinkerTools ably creates a problem space in which numeric, qualitative, and visual representations consistently work together.

Furthermore, Perkins and Unger (1994) suggest that microworlds afford the integration of structuremapping frameworks based on analogies and metaphors. Similarly, a microworld can be designed so as to provide a representation that purposefully directs a student to focus on the most salient relationships of the phenomena being studied. Of course, such benefits do not come without certain costs or risks. For example, if the users do not correctly understand the mapping structure of the analogy, then the benefits will be lost and the students may potentially form misconceptions. Just providing a microworld to students, without the pedagogical underpinnings, should not be expected to lead to learning. The role of the teacher and the resulting classroom practice is crucial here. Microworlds rely on a culture of learning in which students are expected to inquire, test, and justify their understanding. “Students needs to be actively engaged in the construction and assessment of their understandings by working thoughtfully in challenging and reflective problem contexts” (p. 27). As Perkins and Unger (1994) point out, microworld designers have to articulate adequately the components and relationships among components of the domain to be learned. Next they have to construct an illustrative world exemplifying that targeted domain. Finally, the illustrative world should provide natural or familiar referents that, when placed in correspondence with one another and mapped to the target domain, yield a better understanding of the domain. (p. 30)

Constructionism: microworld research evolves

Constructionism is strongly rooted in student-generated projects. Projects offer a way critically to relate motivation and thinking and can be defined as “relatively long-term, problemfocused, and meaningful units of instruction that integrate concepts from a number of disciplines or fields of study” (Blumenfeld et al., 1991, p. 370). Projects have 2 essential components: a driving question or problem and activities that result in one or more artifacts (Blumenfeld et al., 1991). Artifacts are “sharable and critiquable externalizations of students’ cognitive work in classrooms” and “proceed through intermediate phases and are continuously subject to revision and improvement” (Blumenfeld et al., 1991, pp. 370–371).

It is important that the driving question not be overly constrained by the teacher. Instead, students need much room to create and use their own approaches to designing and developing the project. Projects, as external artifacts, are public representations of the students’ solution. The artifacts, developed over time, reflect their understanding of the problem over time aswell. In contrast, traditional school tasks have no driving question and, thus, no authentic purpose to motivate the student to draw or rally the difficult cognitive processes necessary for complex problem- solving.

In an early constructionnism research, Harel and Papert (1991) strongly suggest that what made a difference was not Logo or any particular group of strategies but, rather, that a “total learning environment” (p. 70) was created that permitted a culture of design work to flourish. They particularly point to the affective influences of this environment. These students developed a different “relationship with fractions” (p. 71), that is, they came to like fractions and saw the relevancy of this mathematics to their everyday lives. Many reported “seeing fractions everywhere.” Harel and Papert resist any tendency to report the success as being “caused” by Logo. Instead, “learning how to program and using Logo enabled these students to become more involved in thinking about fractions knowledge” (p. 73). They point to Logo’s allowing such constructions about fractions to take place.

But, successful project-based learning is not a panacea. Success is based on many critical assumptions or characteristics and failure in any one can thwart the experience. Examples include an appreciation of the complex interrelationship between learning and motivation, an emphasis on student-driven questions or problems, and the commitment of the teacher and his/her willingness to organize the classroom to allow the complexities of project-based learning to occur and be supported (Blumenfeld et al., 1991). Fortunately, the recent and continuing development of rich technological tools directly support both teachers and students in the creation and sharing of artifacts.

Students must be sufficiently motivated over a long period to gain the benefits of project-based learning: Among the factors that contribute to this motivation are “whether students find the project to be interesting and valuable, whether they perceive that they have the competence to engage in and complete the project, and whether they focus on learning rather than on outcomes and grades” (Blumenfeld et al. 1991, p. 375).

The teacher’s role is critical in all this:

  • create opportunities for project-based learning,
  • support and guide student learning through scaffolding and modeling,
  • encourage and help students manage learning and metacognitive processes,
  • help students assess their own learning and provide feedback.

Whether teachers will be able to meet these demands depends in large part on

  • their own understanding of the content embedded in projects,
  • their ability to teach and recognition of student difficulty in learning the content (i.e., pedagogical awarenesses),
  • their willingness to assume a constructivist culture in their classrooms.

Examples of microworlds

Many microworlds have become available since 1980:

Repositories
  • MathTools at Math Form. This is a large library (not just for microworlds)

References

  • Abelson, H. (1982). Logo for the Apple II. Peterborough. NH: BYTE/McGraw Hill.
  • Barab, Sasha A. ,Kenneth E. Hay, Michael Barnett and Kurt Squire (2001). Constructing Virtual Worlds: Tracing the Historical Development of Learner Practices, Cognition And Instruction, 19(1), 47-94. PDF
  • Blumenfeld, P. C., Soloway, E., Marx, R. W., Krajcik, J. S., Guzdial, M., & Palinscar, A. (1991). Motivating project-based learning: Sustaining the doing, supporting the learning. Educational Psychologist, 26(3 & 4), 369–398.
  • Clements, D. (1989). Computers in elementary mathematics education. Englewood Cliffs, NJ: Prentice Hall.
  • diSessa, A. A. (1989). Computational media as a foundation for new learning cultures. Technical Report G5. Berkeley: University of California.
  • diSessa, A. A. (2000). Changing minds: Computers, learning, and literacy. Cambridge, MA: MIT Press.
  • diSessa, A. A., Abelson, H., & Ploger, D. (1991). An overview of Boxer. Journal of Mathematical Behavior, 10, 3–15.
  • Edwards, L. D. (1995). Microworlds as representations. In A. A. diSessa, C. Hoyles, R. Noss, & L. D. Edwards (Eds.), Computers and exploratory learning (pp. 127–154). New York: Springer.
  • Feurzeig, W., & Roberts, N. (1999). Introduction. In W. Feurzeig & N. Roberts (Eds.), Modeling and simulation in science and mathematics education (pp. xv–xviii). New York: Springer-Verlag.
  • Harel, I., & Papert, S. (1991). Software design as a learning environment. In I. Harel & S. Papert (Eds.), Constructionism (pp. 41–84). Norwood, NJ: Ablex.
  • Horwitz, P., & Christie, M. A. (2000). Computer-based manipulatives for teaching scientific reasoning: An example. In M. J. Jacobson & R. B. Kozma (Eds.), Learning the sciences of the 21st century: Research, design, and implementing advanced technology learning environments (pp. 163–191). Mahwah, NJ: Lawrence Erlbaum Associates.
  • Horwitz, P., & Christie, M. A. (2002, April). Hypermodels: Embedding curriculum and assessment in computer-based manipulatives. Paper presented at the annual meeting of the American Educational Research Association, New Orleans, LA.
  • Jackson, S., Stratford, S. J., Krajcik, J. S., & Soloway, E. (1996). Making dynamic modeling accessible to pre-college science students. Interactive Learning Environments, 4(3), 233–257.
  • Jonassen, D. (1991). Objectivism versus constructivism: Do we need a new philosophical paradigm? Educational Technology Research & Development, 39(3), 5–14.
  • Jonassen, D. H. (1996). Computers in the classroom: Mindtools for critical thinking. Upper Saddle River, NJ: Prentice Hall.
  • Joolingen van, Dr. W.R. and King, S. and Jong de, Prof. dr. T. (1997) The SimQuest authoring system for simulation-based discovery learning. In: B. du Boulay & R. Mizoguchi (Eds.), Artificial intelligence and education: Knowledge and media in learning systems. IOS Press, Amsterdam, pp. 79-86. PDF
  • Norman, D. A. (1993). Things that make us smart: Defending human attributes in the age of the machine. Reading, MA: Addison–Wesley.
  • Ogborn, J. (1999). Modeling clay for thinking and learning. In W. Feurzeig & N. Roberts (Eds.), Modeling and simulation in science and mathematics education (pp. 5–37). New York: Springer- Verlag.
  • Olive, J. (1998). Opportunities to explore and integrate mathematics with “The Geometer’s Sketchpad.” In R. Lehrer & D. Chazan (Eds.), Designing learning environments for developing understanding of geometry and space (pp. 395–418). Mahwah, NJ: Lawrence Erlbaum Associates.
  • Papert, S. (1980). Computer-based microworlds as incubators for powerful ideas. In R. Taylor (Ed.), The computer in the school: Tutor, tool, tutee (pp. 203–210). New York: Teacher’s College Press.
  • Resnick, M. (1991). Overcoming the centralized mindset: Towards an understanding of emergent phenomena. In I. Harel & S. Papert (Eds.), Constructionism (pp. 204–214). Norwood, NJ: Ablex.
  • Resnick, M. (1999). Decentralized modeling and decentralized thinking. In W. Feurzeig & N. Roberts (Eds.), Modeling and simulation in science and mathematics education (pp. 114–137). New York: Springer-Verlag.
  • Richmond, B., & Peterson, S. (1996). STELLA: An introduction to systems thinking. Hanover, NJ: High Performance Systems.

Van Joolingen

  • Rieber, L. P. (1996) Microworlds, in Jonassen, David, H. (ed.) Handbook of research on educational communications and technology. Handbook of Research for Educational Communications and Technology. Second edition. Simon and Schuster, 583-603 ISBN 0-02-864663-0
  • Rieber, L. P. (1996b). Seriously considering play: Designing interactive learning environments based on the blending of microworlds, simulations, and games. Educational Technology Research & Development, 44(2), 43–58.
  • Roschelle, J., Kaput, J., & Stroup, W. (2000). SimCalc: Accelerating student engagement with the mathematics of change. In M. J. Jacobson & R. B. Kozma (Eds.), Learning the sciences of the 21st century: Research, design, and implementing advanced technology learning environments (pp. 47–75). Mahwah, NJ: Lawrence Erlbaum Associates.
  • Salomon, G., Perkins, D. N., & Globerson, T. (1991). Partners in cognition: Extending human intelligence with intelligent technologies. Educational Researcher, 20(3), 2–9.
  • Spitulnik, M. W., Krajcik, J. S., & Soloway, E. (1999). Construction of models to promote scientific understanding. In W. Feurzeig & N. Roberts (Eds.), Modeling and simulation in science and mathematics education (pp. 70–94). New York: Springer-Verlag.
  • White, B. Y. (1992). A microworld-based approach to science education. In E. Scanlon & T. O'Shea (Eds.), New directions in educational technology (pp. 227-242). New York: Springer-Verlag.
  • White, B. Y. (1993). ThinkerTools: Causal models, conceptual change, and science education. Cognition and Instruction, 10(1), 1–100.
  • Wilensky, U., & Stroup,W. (2002, April). Participatory simulations: Envisioning the networked classroom as a way to support systems learning for all. Paper presented at the annual meeting of the American Educational Research Association, New Orleans, LA.
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