This study tests the use of computer-assisted grading rubrics compared to other grading methods with respect to the efficiency and effectiveness of different grading processes for subjective assignments. The test was performed on a large Introduction to Business course. The students in this course were randomly assigned to four treatment groups based on the grading method. Efficiency was measured by the professor's time to grade the assignments; effectiveness was measured by a student satisfaction survey. Results suggest that the computer-assisted grading rubrics were almost 200% faster than traditional hand grading without rubrics, more than 300% faster than hand grading with rubrics, and nearly 350% faster than typing the feedback into a Learning Content Management System. Results also seemed to indicate that the use of a computer-assisted grading rubric did not negatively affect student attitudes concerning the helpfulness of their feedback, their satisfaction with the speed with which they received their feedback, or their satisfaction with the method by which they received feedback.

As most interior point algorithms iterate, they repeatedly perform costly matrix operations, such as projections, on the entire constraint matrix. For large-scale linear programming problems, such operations consume the great majority of the computation time required. However, for problems where the number of variables far exceeds the number of constraints, operations over the entire constraint matrix are unnecessary. We will examine and extend decomposition techniques which greatly reduce the amount of work required by such interior point methods as the dual affine scaling and the dual potential reduction algorithms. In an effort to judge the practical viability of the decompositioning, we compare the performance of the dual potential reduction algorithm with and without decompositioning over a set of randomly generated transportation problems. Accompanying a theoretical justification of these techniques, we focus on the implementation details and computational results of one such technique.linear programming, potential reduction algorithms, transportation problem, decomposition, short-cut technique

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A computational study of some logarithmic barrier decomposition algorithms for semi‐infinite programming is presented in this paper. The conceptual algorithm is a straightforward adaptation of the logarithmic barrier cutting plane algorithm which was presented recently by den Hartog et al. (Annals of Operations Research, 58, 69–98, 1995), to solve semi‐infinite programming problems. Usually decomposition (cutting plane methods) use cutting planes to improve the localization of the given problem. In this paper we propose an extension which uses linear cuts to solve large scale, difficult real world problems. This algorithm uses both static and (doubly) dynamic enumeration of the parameter space and allows for multiple cuts to be simultaneously added for larger/difficult problems. The algorithm is implemented both on sequential and parallel computers. Implementation issues and parallelization strategies are discussed and encouraging computational results are presented.