In this issue we introduce hypermodels: environments for learning fundamental concepts through guided inquiry under the control of scripts. Hypermodels, when used with powerful software tools and carefully designed scripts, represent an important new approach to learning that has major implications for schools and researchers.

Our front page article recounts the genesis of hypermodels, which Paul Horwitz invented to solve a problem of knowledge transfer with GenScope⁵, his genetics simulation package. As he improved GenScope, Paul realized that his solution could be applied to other simulation and probeware tools we were developing. Consequently, we are adding Pedagogica to our Molecular Workbench⁶ project to create a hypermodel for teaching about atoms, molecules, and the macroscopic consequences of their forces and interactions (see article, page 4). And, we will add Pedagogica to our probeware and simulations to guide student learning about heat, temperature, and heat flow (see article, page 6). We hope to collaborate with others to generate a dozen hyper- models that can address most of the fundamental concepts within math and science.

We are excited about hypermodels because they solve a vexing set of practical problems that seem to have impeded the widespread application of content-rich tools, one of the most important categories of educational technologies. Many projects have demonstrated the value of software environments that incorporate important content that students learn through interaction and exploration (e.g., probeware, GenScope, ThinkerToys, Model-It). The impact of these and similar tools is diminished by the unrealistic demands they place on teachers: the tools require time to learn, they are difficult to convert into inquiry-based lessons, and learning that results from student interactions with these tools is difficult to assess.

It is natural for developers to build into their tools an array of capabilities and options that exploit the power of modern computers. But the result may be baffling to the beginner. In the past, we have tried to simplify these tools with help screens, online manuals, and scaffolding - guidance that can be removed when no longer useful. Too often, however, all this assistance is only makes it more confusing.

Pedagogica is like a puppet master who is controlling the puppet application's actions. Pedagogica can make puppets run or crawl, talk and listen, defy gravity, and even change appearance. It can control what the screen looks like and what options the user can access. It can pose a situation for the learner to explore, provide guidance, ask questions, and monitor progress. This monitoring can provide detailed assessment of student progress and problem solving strategies. Pedagogica is itself controlled by a script, so the entire user interaction can be easily changed in response to formative feedback or student learning. In a research setting, we can test different instructional strategies on students in the same class and obtain detailed data. It is possible to perform studies at a distance because both the scripts and the data can be communicated over the Internet.

Hypermodels share some characteristics with CAI (computer assisted instruction) applications, which also control what the learner sees, evaluate progress, and, if they are "intelligent," can adapt to student responses and learning styles. The critical difference is that hypermodels have at their core a sophisticated tool that students can use to learn content through exploration and inquiry; a constructivist educational strategy. In contrast, CAI⁷ software is usually much more directive and "instructivist."

Our underlying tools embody a pure constructivist philosophy that permits students to learn through open-ended exploration. Even though this type of learning is powerful, students can take too much time and miss important topics and the tool can be difficult to disseminate and confusing for beginners. Pedagogica converts the tool into a hypermodel that is somewhat instructivist, because the script constrains the tool and guides the learner to discover specific concepts that a curriculum developer has selected. Done well, students still learn through their own explorations, but within constrained domains and with guidance that ensures that most students discover the important concepts.

Much of our past research has been dedicated to demonstrating the learning potential inherent in powerful tools. Well designed tools permit students to understand basic abstract concepts through interaction and exploration. Traditionally, many concepts have been taught only at advanced levels using abstract mathematics. If attempted at lower levels, the treatment is qualitative and verbal, too often requiring students to memorize apparently unrelated facts and sometimes reinforcing student misconceptions.

For most students, fundamental understandings that could simplify and unify science are qualitative and intuitive. The problem has been that it is difficult to develop a student's scientific intuitions. Intuition appears to be based on interacting with ideas, exploring, and making mistakes. It is often based on understanding dynamic situations involving chains of cause and effect. Other situations include randomness (e.g., temperature), hidden levels (e.g., genetics), or the emergent behavior of a system that depends on details of objects and their interactions (e.g., a flu epidemic). Lectures, illustrations, proofs, derivations, and even movies fail to provide the needed interactivity or to clarify the cause-and-effect relationships essential to building intuition. Appropriately designed software tools, however, can.

It is important to teach basic concepts earlier, not only because science is always adding content, but because an understanding of basic concepts can simplify learning by providing more links and causal relationships. Learning based on basic concepts should require less memorization, last longer, and facilitate further learning. Six to twelve powerful software tools could give beginning students an intuitive understanding of all the basic concepts of science. By concentrating on these areas instead of on the hundreds of standards and benchmarks, students could learn science in an integrated, logical way.

Hypermodels can revitalize education by concentrating on guided inquiry into core topics that have great explanatory power. While there can be debate about what the core topics are, the important point is that it is possible to envision a curriculum based on a small number of core ideas. A curriculum structured in this way, based on hypermodels, allows time for inquiry-based learning that is increasingly ignored in the rush to cover all the required standards.

Robert Tinker is president of The Concord Consortium.
bob@concord.org⁸

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Last Updated: April 21, 2001
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