MODEL ELICITING ACTIVITIES: LESSONS LEARNED FROM A FIVE-YEAR, SEVEN INSTITUTION COLLABORATION

In practice, engineers typically have to model physical processes and communicate their findings to a broad audience. Unfortunately, many engineering courses tend to stress solving over constrained homework problems using algorithmic substitution, and rarely ask students to effectively communicate their findings. Model Eliciting Activities (MEAs), which were developed in the mathematics educational community, attempt to address these shortcomings by asking teams of students to attack open ended problems. MEAs are distinctly different from “textbook” problem solving activities in terms of length of time, access to information resources, number of individuals involved in the problem solving process, and type of documentation required. However, the most important difference is the emphasis on building, expressing, testing and revising conceptual models.

Six guiding principles of MEAs try to maximize the learning potential of these activities:

Reality Principle contains a realistic client with engineering context
Model Construction Principle a mathematical model and/or decision algorithm must be developed
Model Documentation Principle a deliverable, often in the form of a memo to the client, should reveal student thinking
Self Assessment Principle students should be able to know when their model is “good enough”
Generalizability Principle the model should apply to multiple related situations
Effective Prototype Principle the MEA should involve important concepts that are critical in future classes and engineering practice.

Our seven institution collaboration, funded in part by a Type III National Science Foundation CCLI Grant, has teamed to expand the use of MEAs in engineering disciplines. Our research program includes creating and disseminating new MEAs (www.modelsandmodeling.net) and extending the MEA construct to address conceptual understanding, ethical considerations, and the use of physical artifacts to improve MEA efficacy. Example MEAs include creating an accident reconstruction tool for police in Sri Lanka, constructing an energy rebate program for a local utility company, and developing a sizing algorithm for wind turbine blades. Results show improved conceptual understanding as well as improved self-reported abilities in writing, working in teams, knowing professional and ethical responsibilities, and interpreting engineering data. We believe that MEAs also have the potential to improve student motivation and increase long term retention.