by Dr. David Jonassen

First published September 2002.

Model Building and Learning

Learning is most meaningful when it is intentional. All human behavior is goal directed (Schank, 1994). That is, everything that we do is intended to fulfill some goal. When learners are actively and willfully trying to achieve a cognitive goal, they think and learn more because they are fulfilling an intention. The most intentional way of using technology to learn, I believe, is to build models of the phenomena that students are studying. Why? Because the models are representative of the theories that students are constructing about the world around them. Those theories are also known as mental models, so technology-based computational models reflect students’ mental models as they are being constructed.

It is quite common in maths, science, and engineering for students to use models in their learning. Most science textbooks present a model of some phenomenon for students to comprehend. They follow up the model with well-structured problems related to those models for learners to solve. This form of model-based reasoning cannot be as effective as model building because the students are not actually building the models. Model-building refers to student construction, manipulation, or testing of models relating to the phenomena they are studying.

Learning Outcomes

Model-building can focus on different kinds of learning outcomes. They can be used by learners to model domain knowledge, such as databases of cell types in the human body, semantic networks of concepts in economics, and microworlds for supporting construction of geometry theorems. Models can also represent different kinds of problems, such as systems modes chemistry problems, expert systems of troubleshooting problems, or spreadsheets of electrical engineering problems.

Students can also build models of systems, such as respiratory system, ecosystems, or species of animals. Tools such as databases and semantic networking tools are useful for building models of the semantic structure of knowledge domains. Finally, students can build models of the thinking processes that are required to perform some task, otherwise known as cognitive simulations. Expert systems can be used to represent the thinking required in different decision-making activities or systems models of strategies for organizing ideas.

Mindtools

The tools used to construct these models are otherwise known as cognitive tools, or Mindtools (Jonassen, 2000). Mindtools are software programs that provide multiple formalisms for representing knowledge. They engage different kinds of critical, creative, and complex thinking. Mindtools include semantic organization tools (databases, semantic networks), dynamic modeling tools (spreadsheets, expert systems, systems modeling tools, and microworlds), information interpretation tools and visualization tools, knowledge construction tools (multimedia production, hypermedia construction and linking, Web site production), and conversation tools (synchronous communication environments, asynchronous information tools, scaffolded computer conferences).

When Mindtools are used to build student models of understanding, those models can be used for student assessment, and as collaborative learning activities, planning and analysis tools, follow-ups to situated activity, and as conversation media. Mindtools for model building are effective because learners are designers engaged in constructing personal meaning that makes the learners intellectual partners with the technology. Mindtools use inexpensive, commonly available technologies; they can be applied to virtually any content domain; and they are readily learned.

Author

Dr. David Jonassen
Distinguished Professor, School of Information Science & Learning Technologies, University of Missouri-Columbia