Project Abstract

Simulation and Modeling in Technology Education (SMTE) is a five-year project that develops and researches the academic potential of a hybrid instructional model that infuses computer simulations, modeling, and educational gaming into middle school technology education programs. These prototypical materials use 3-D simulations and educational gaming to support students learning STEM content and skills through developing solutions to design challenges. The virtual environment allows students to analyze and improve their designs by changing variables and observing how their changes affect design performance. Once the designs are optimized on-screen, students will construct physical models and compare their functionality and effectiveness to the simulated virtual models. A uniqueness of the Project is the development of an innovative Web-based instructor design interface and a library of objects to enable instructors to modify the context of the design problems to fit different instructional and geographic settings. The research investigates the transferability of the model and its potential to improve STEM teaching and learning.

The project incorporates a cyber-enabled engineering design approach that integrates science and mathematics and uses contemporary technological tools and pedagogical strategies. The content is driven by the concepts and skills identified in the K-12 Standards for Technological Literacy (STL). The partners are Hofstra University’s Center for Technological Literacy, the State University of New York at Buffalo, the NSF National Center for Telecommunications Technologies, Bloomsburg University in Pennsylvania, and the City University of New York.

The research design includes a test of three conditions: the experimental (hybrid) condition; the gaming and simulation only condition that involves students in the virtual tasks only; and students doing physical modeling task. Testing will involve 12 teachers and 240 students with data collected in six assessment domains. Data will be analyzed using a variety of multivariate statistical analyses.