We report the results of a year-long experiment in the use of robots to teach computer science. Our data set compares results from over 800 students on identical tests from both robotics and nonrobotics-based laboratory sessions. We also examine the effectiveness of robots in encouraging students to select computer science or computer engineering as a field of study. Our results are negative: test scores were lower in the robotics sections than in the nonrobotics ones, nor did the use of robots have any measurable effect on students' choice of discipline. We believe the most significant factor that accounts for this is the lack of a simulator for our robotics programming system. Students in robotics sections must run and debug their programs on robots during assigned lab times, and are therefore deprived of both reflective time and the rapid compile-run-debug cycle outside of class that is an important part of the learning process. We discuss this and other issues, and suggest directions for future work.

Games can be a valuable tool for enriching computer science education, since they can facilitate a number of conditions that promote learning: student motivation, active learning, adaptivity, collaboration, and simulation. Additionally, they provide the instructor the ability to collect learning metrics with relative ease.

We present one approach to teaching basic computer science concepts with robotics, using an Ada interface to Lego Mindstorms™ 1 . We show simple problems put to students with no programming experience, discuss the solutions, and for each concept explain the advantages of using robots to teach it.

Improving management of inpatients with diabetes undergoing vascular surgery requires collaboration among many health care practitioners. This article describes a performance improvement project that implemented two evidence-based algorithmic order sets to guide perioperative glucose management for diabetic patients undergoing vascular procedures and utilized a certified diabetes educator (CDE) to educate health care practitioners. Results showed statistically and clinically significant reductions in infection and differences in mean blood glucose between pre- and postintervention groups, including a direct relationship between glucose control and the level of involvement of a CDE in patient care.

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We report the results of a year-long experiment in the use of robots to teach computer science. Our data set compares results from over 800 students on identical tests from both robotics and non-robotics based laboratory sessions. We also examine the effectiveness of robots in encouraging students to select computer science or computer engineering as a field of study.Our results are negative: test scores were lower in the robotics sections than in the non-robotics ones, nor did the use of robots have any measurable effect on students choice of discipline. We believe the most significant factor that accounts for this is the lack of a simulator for our robotics programming system. Students in robotics sections must run and debug their programs on robots during assigned lab times, and are therefore deprived of both reflective time and the rapid compile-run-debug cycle outside of class that is an important part of the learning process. We discuss this and other issues, and suggest directions for future work.

Efforts to predict polypeptide structures nearly always assume that the native conformation corresponds to the global minimum free energy state of the system. Given this assumption, a necessary step in solving the problem is the development of efficient global energy minimization techniques. We describe a hybrid genetic algorithm which incorporates efficient gradient-based minimization directly in the fitness evaluation, which is based on a general full-atom potential energy model. The algorithm includes a replacement frequency parameter which specifies the probability with which an individual is replaced by its minimized counterpart. Thus, the algorithm can implement either Baldwinian, Lamarckian, or probabilistically Lamarckian evolution.We also describe experiments comparing the effectiveness of the genetic algorithm with and without the local minimization operator, with various probabilities of replacement. The experiments apply the techniques to the minimization of the CHARMM potential for [Met]-Enkephalin.When fitness proportionate selection is used, the Baldwinian, Lamarckian, and probabilistically Lamarckian approaches obtain better energies (and better basins of attraction) than the standard genetic algorithm. This suggests that the low-energy local minima in polypeptide energy landscapes occur sufficiently regularly to benefit from the proposed hybrid approaches. When tournament selection is used, the results are qualitatively similar, except that the hybrid approaches are prone to premature convergence. Increasing replacement frequency reduces the tendency toward premature convergence for the experiments performed here.