Many individual instructors -- and, in some cases, entire universities -- are gravitating towards the use of comprehensive learning management systems (LMSs), such as Blackboard and Moodle, for managing courses and enhancing student learning. As useful as LMSs are, they are short on features that meet certain needs specific to computer science education. On the other hand, computer science educators have developed--and continue to develop-computer-based software tools that aid in management, teaching, and/or learning in computer science courses. In this report we provide an overview of current CS specific on-line learning resources and guidance on how one might best go about extending an LMS to include such tools and resources. We refer to an LMS that is extended specifically for computer science education as a Computing Augmented Learning Management System, or CALMS. We also discuss sound pedagogical practices and some practical and technical principles for building a CALMS. However, we do not go into details of creating a plug-in for some specific LMS. Further, the report does not favor one LMS over another as the foundation for a CALMS.
Many commercial or open-source systems for organizing courses are available, offering access to course materials, communication support, and receiving and grading student submissions. However, most of these systems are by default not ideally prepared to address specific demands of Computer Science (CS) education. We explore how Moodle as one of the most popular and free systems can be better adapted to support the needs of CS education and provide concrete guidance on features and extensions that could be explored. This report and work based on it can significantly improve courses for educators and students alike.
Simulated annealing's high computational intensity has stimulated researchers to experiment with various parallel and distributed simulated annealing algorithms for shared memory, message-passing, and hybrid-parallel platforms. MapReduce is an emerging distributed computing framework for large-scale data processing on clusters of commodity servers; to our knowledge, MapReduce has not been used for simulated annealing yet. In this paper, we investigate the applicability of MapReduce to distributed simulated annealing in general, and to the TSP in particular. We (i) design six algorithmic patterns of distributed simulated annealing with MapReduce, (ii) instantiate the patterns into MR implementations to solve a sample TSP problem, and (iii) evaluate the solution quality and the speedup of the implementations on a cloud computing platform, Amazon's Elastic MapReduce. Some of our patterns integrate simulated annealing with genetic algorithms. The paper can be beneficial for those interested in the potential of MapReduce in computationally intensive nature-inspired methods in general and simulated annealing in particular.
In this paper we propose a generic pipeline for all-pairs computations on a cluster of workstations. We use this generic pipeline to derive specific cluster algorithms for three different all-pairs problems: n-body simulation, bubble sort, and Gaussian elimination. We implement the generic pipeline and its derivatives on a cluster of Intel Pentium II workstations using C and the PVM cluster computing environment. We measure and evaluate the performance of the derived algorithms. The n-body and bubble sort algorithms achieve super-linear speedup for large problems.
Freedom to choose what, when, and how to contribute in a learning process can motivate students to actively engage and achieve more in their studies. However, freedom of choice complicates course management and may deter instructors from allowing such freedom. Our approach is to utilize existing functionality of course management systems such as Moodle to automatically facilitate and coordinate free student choices and provide much needed relief for instructors at the same time. Using Moodle we have developed novel digital study packs that blend freedom of choice with guidance and control. Our survey shows that assisted freedom of choice is ranked highest in 51% of student responses-in contrast to unlimited choice at 28% or no choice at all at 21%. Experience reported in this paper may be beneficial for instructors who would like to expand their courses with new motivational learning techniques.
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