|Title:||Integrated Software for Artificial Intelligence Tutoring and Assessment in Science|
|Principal Investigator:||Johnson, Benny||Awardee:||Quantum Simulations, Inc.|
|Program:||Science, Technology, Engineering, and Mathematics (STEM) Education [Program Details]|
|Award Period:||3 years||Award Amount:||$1,000,000|
|Type:||Development and Innovation||Award Number:||R305A070067|
Purpose: Compared to students in other industrialized countries, U.S. students perform poorly in studies of achievement in high school chemistry, a core subject in the physical sciences course sequence in U.S. high schools. The purpose of this project is to complete the development of a computer-based tutoring and assessment system for a first-year chemistry course, and to obtain evidence of the system's potential to improve student learning and achievement in chemistry.
Project Activities: In previous work, the researchers completed 10 chemistry topics for this computer-based learning and assessment system. In this project, they are developing three additional modules (Kinetics, Thermochemistry, and Solution Stoichiometry) to yield a comprehensive chemistry curriculum. In addition, the researchers are conducting a study using random assignment to assess the potential efficacy of the computer-based learning and assessment system for improving student achievement in chemistry.
Products: Products include a computer-based learning and assessment system for high school chemistry and reports of the potential efficacy of this system.
Purpose: The purpose of this project is to complete the development of a computer-based tutoring and assessment system for a first-year chemistry course, and to obtain evidence of the system's potential to improve student learning and achievement in chemistry.
Setting: High schools from both urban and rural settings in several states are participating, including California, Kansas, Kentucky, Maryland, New Mexico, Ohio, Oklahoma, and Pennsylvania.
Population: The population includes high school chemistry teachers and students. The participating schools are selected to ensure a diverse population with regard to geographic location, teacher experience, and student characteristics.
Intervention: This computer-based learning and assessment system is designed to provide feedback to students while they are trying to solve chemistry problems. In addition, the system generates reports for teachers and students that analyze student learning and performance. Previously, the researchers completed 10 chemistry modules for this computer-based system. In this project, they are developing three new modules (Kinetics, Thermochemistry, and Solution Stoichiometry) to yield a comprehensive chemistry curriculum.
Research Design and Methods: The study uses a pre- and post-test, comparison design. Fifteen to twenty chemistry teachers will have one of their chemistry classes randomly assigned to the treatment condition and a second class to the control condition. In the treatment group, the teacher and students use the computer-based tutoring and assessment programs along with normal instruction.
Control Condition: The control group receives conventional chemistry instruction, without the computer-based programs.
Key Measures: The primary outcome measure is student achievement in chemistry.
Data Analytic Strategy: This development project is intended only to obtain evidence of the potential efficacy of the intervention; initial analyses will be at the level of the student.
Related IES Projects: Integrated Software for Artificial Intelligence Tutoring and Assessment in Science (R305K040008) and A Randomized Controlled Study of the Effects of Intelligent Online Chemistry Tutors in Urban California School Districts (R305A080063)
Journal article, monograph, or newsletter
Johnson, B.G., and Holder, D.A. (2010). A Model-Tracing Intelligent Tutoring System for Oxidation Number Assignment. The Chemical Educator, 15: 447–454.
Kuhel, J.J., Wheeler, M.C., Miele, P.E., Holder, D.A., Johnson, B.G., Paterno Parsi, A.A., and Madura, J.D. (2010). Quantitative Impact of an Artificial Intelligence Tutoring System on Student Performance in Assigning Oxidation Numbers in Chemical Formulas. The Chemical Educator, 15: 455–460.