25,439 research outputs found

    Pervasive Parallel And Distributed Computing In A Liberal Arts College Curriculum

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    We present a model for incorporating parallel and distributed computing (PDC) throughout an undergraduate CS curriculum. Our curriculum is designed to introduce students early to parallel and distributed computing topics and to expose students to these topics repeatedly in the context of a wide variety of CS courses. The key to our approach is the development of a required intermediate-level course that serves as a introduction to computer systems and parallel computing. It serves as a requirement for every CS major and minor and is a prerequisite to upper-level courses that expand on parallel and distributed computing topics in different contexts. With the addition of this new course, we are able to easily make room in upper-level courses to add and expand parallel and distributed computing topics. The goal of our curricular design is to ensure that every graduating CS major has exposure to parallel and distributed computing, with both a breadth and depth of coverage. Our curriculum is particularly designed for the constraints of a small liberal arts college, however, much of its ideas and its design are applicable to any undergraduate CS curriculum

    Challenging the Computational Metaphor: Implications for How We Think

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    This paper explores the role of the traditional computational metaphor in our thinking as computer scientists, its influence on epistemological styles, and its implications for our understanding of cognition. It proposes to replace the conventional metaphor--a sequence of steps--with the notion of a community of interacting entities, and examines the ramifications of such a shift on these various ways in which we think

    Quality Enhancement Themes: the First Year Experience. Curriculum Design for the First Year

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    This report outlines the work and outcomes of a practice-focused development project 'Curriculum design for the first year'. The project was one of nine funded by the Quality Assurance Agency for Higher Education (QAA) under the First-Year Experience Enhancement Theme of the Scottish quality enhancement agenda. The stages of this curriculum design project included: completing a literature review; running staff workshops to gather and disseminate information; holding student focus groups to gather students, views and experiences of the curriculum; collecting case studies of interest to the sector; and reporting findings to the sector. Key findings from the literature are presented in this report. They include the need to adopt student-centred active learning strategies (Harvey, Drew and Smith, 2006; Oliver-Hoyo and Allen, 2005; Barefoot, 2002) and the importance of providing early formative feedback to students (Davidson and Young, 2005; Barefoot, 2002). Many suggestions for improving learning and teaching strategies have been adopted at module level, but could be implemented strategically across the breadth of a programme curriculum. Kift and Nelson (2005) supported this view and argued that it is equally important to support these principles with systemic university-wide change, including administrative and support programmes that are also integrated with the curriculum and student needs

    A Project Based Approach to Statistics and Data Science

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    In an increasingly data-driven world, facility with statistics is more important than ever for our students. At institutions without a statistician, it often falls to the mathematics faculty to teach statistics courses. This paper presents a model that a mathematician asked to teach statistics can follow. This model entails connecting with faculty from numerous departments on campus to develop a list of topics, building a repository of real-world datasets from these faculty, and creating projects where students interface with these datasets to write lab reports aimed at consumers of statistics in other disciplines. The end result is students who are well prepared for interdisciplinary research, who are accustomed to coping with the idiosyncrasies of real data, and who have sharpened their technical writing and speaking skills
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