Identifying Informatively Easy and Informatively Hard Concepts

In this article, we leverage ideas from the theory of coevolutionary computation to analyze interactions of students with problems. We introduce the idea of informatively easy or hard concepts. Our approach is different from more traditional analyses of problem difficulty such as item analysis in the sense that we consider Pareto dominance relationships within the multidimensional structure of student–problem performance data rather than average performance measures. This method allows us to uncover not just the problems on which students are struggling but also the variety of difficulties different students face. Our approach is to apply methods from the Dimension Extraction Coevolutionary Algorithm to analyze problem-solving logs of students generated when they use an online software tutoring suite for introductory computer programming called problets . The results of our analysis not only have implications for how to scale up and improve adaptive tutoring software but also have the promise of contributing to the identification of common misconceptions held by students and thus, eventually, to the construction of a concept inventory for introductory programming.

A Study on Modeling Error Estimation for Mobile Robot Based on Coevolutionary Computation and Image Processing

Evolutionary Computation (EC) is used to minimize modeling errors between robotic movement in computer simulation and trajectories of an actual robot. Generally, this task is important and so difficult. This paper proposes the method to minimize the modeling error between robotic movements and simulation results using coevolutionary computations with image processing technique. In the proposed method, a video camera on the ceiling captures robot movement, actual robot trajectories are detected from captured images by image processing, and modeling errors are estimated. Results of the experiments using an actual robot confirmed the effectiveness of our proposal and showed that modeling errors are reduced effectively. The sections that follow detail the problems overcome and our projected work.