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Junior Lazar Nikolic, a computer science major from Roswell, Ga., has been awarded the James P. Schwar Prize for the upcoming summer.

The honor includes a cash prize and an EXCEL Scholarship in computer science. Schwar was professor of computer science at Lafayette from 1962-2000.

Earlier this year, Nikolic teamed up with Daniel Swarr ’03 (Clifton Park, N.Y.) and Guangxi Wang ’03 (Shanghai, China) to place among the top 14 percent of participating teams — earning the second-highest rating — in the 17th annual Mathematical Contest in Modeling, an international competition sponsored by the Consortium for Mathematics and its Applications.

In Lafayette’s distinctive EXCEL Scholars program, students assist faculty members with research while earning a stipend. Nikolic will be working with Chun Wai Liew, assistant professor of computer science, to develop a computer-based tutoring program for computer science and physics courses. His work will include analysis of data gathered from this spring’s Physics 131 class and involvement in all phases of academic research, including design and analysis of experiments.

The microprocessor revolution ushered in many attempts to improve learning through computer-based tutoring systems, notes Liew. The perceived advantages for students include greater freedom to schedule their time, proceed at their own pace, and repeat lessons and exercises until they are fully mastered. However, the inflexibility of the computer does not allow for feedback that would help students resolve problems, not does it allow those who quickly grasp the material to skip sections they do not need.

“The goal of the TUTOR project is to develop a computer-based tutoring system that will be much more flexible and adaptive,” says Liew. “The plan is to use Artificial Intelligence techniquesthat will be able to interact with a student in the following manner: analyze and critique a student’s answer to a problem; categorize the mistake (if present) that the student made; and formulate a set of lessons and questions that will help the student correct his/her mistake. This might involve a lesson or module on earlier concepts to refresh the student’s memory.”

If the results are positive, they could enhance computer-based tutoring systems in areas other than computer science and physics as well. “Because students generally learn better if they are required to implement classroom concepts in laboratory exercises,” says Liew, “the TUTOR exercises will reinforce the concepts taught by the instructor, providing the students with immediate and relevant feedback.” Liew already has presented results of experiments on sample introductory physics problems at several Artificial Intelligence conferences.

Liew joined the Lafayette faculty in 1995 after teaching in the departments of electrical and computer engineering and computer science at Rutgers University, New Brunswick, N.J. He also has been a software consultant and analyst. Liew earned a bachelor’s degree in electrical engineering from Cornell University in 1979 and a Ph.D. in computer science from Rutgers.

Categorized in: Academic News