University of Houston

Expert systems are a type of software designed to clarify uncertainties in areas that typically involve a human expert. Instructors usually make the decision to adopt classroom materials, assignments, and teaching methods for their courses. However, instructors may encounter difficulty in determining whether their choices are effective or synergistic with each other. Our work aims to produce an artificially intelligent evaluation system which can find and produce inferences in student survey data using concepts from machine learning and data mining and report those findings in a human-readable format to aid decision makers. Our findings give us confidence that a system of this type can benefit educators and students in decision making. For example, by informing the users of positive and negative relations which arise from our class measures; instructors can consider adopting a new textbook, using a new classroom technology, or giving more of one type of assignment over another. Students can use this information to decide which course materials lead to higher exam performance and find optimal ways to study. This work falls under the emerging field of educational data mining, as it serves to provide new information to educators and students by examining learning environments, teaching methods, and student factors to formulate insights into the overall curriculum. Our platform is web-based and can thus be accessed and used remotely by instructors worldwide. The goal is to provide instructors with an easy to administer and easy to understand evaluation system which can be deployed anywhere at any time.