Integrating Creative Writing and Computational Thinking to Develop Interdisciplinary Connections

A typical college curriculum does not make it easy for students to establish connections between required general education courses and courses in their majors. Intentional linking of courses from different disciplines using interdisciplinary pedagogical strategies allows students to make those connections while developing the interdisciplinary skills which will benefit their college and post-college careers. In addition to communication, critical thinking and reasoning, and collaborative skills, it has been recently argued that computational thinking (i.e., the application of computing concepts and methods to solve problems) should also be a part of a twenty-first century liberal education for a broad range of college students, including those not majoring in computing. Computational thinking concepts and skills can help students frame problems in a variety of fields and disciplines (not just STEM disciplines) using novel strategies, and, in so doing, to become better problem solvers in their professions. At our institution, many students not majoring in computing (or a STEM discipline) take a first-year problem-solving with computer programming course (PS), which is designed for Computer Science majors, to satisfy the computer literacy/fluency requirement in their degree or to learn computational thinking concepts and skills. However, since PS is a gateway course for Computer Science majors, it is even more challenging for non-majors, resulting in high non-passing and withdrawal rates. To integrate computational thinking in required liberal arts courses, we created a general education interdisciplinary course, Programming Narratives: Computer Animated Storytelling, aimed at non-computer majors, which emphasizes creative writing and computational thinking. In this interdisciplinary course, students learn the structure of narrative, concepts of problem solving, and the logic of computer programming languages as they develop a narrative-driven video game prototype. This process helps students achieve the college-wide learning goal of making meaningful and multiple connections among the liberal arts majors, as well as between the liberal arts and the areas of study leading to a major or profession. Our findings suggest that the learning objectives and the pedagogical approaches used in the course are adequate for a broad range of non-computer majors. Performance on writing and computing assessments as well as final grades (75% of students obtained a grade of C or better) indicated that a vast majority of students successfully achieved the learning objectives. These results were consistent with student perceptions as reflected in an end-of-course survey. There is also evidence that students satisfactorily integrated creative writing and computer programming to develop their video game prototypes, making in-depth interdisciplinary connections along the way. We believe that this intentional emphasis on connections between disciplines develops the interdisciplinary skills and perspectives which are important for graduation, and it lays the groundwork for interdisciplinary thinking in the workplace

Curriculum Guidelines for Undergraduate Programs in Data Science

The Park City Math Institute (PCMI) 2016 Summer Undergraduate Faculty Program met for the purpose of composing guidelines for undergraduate programs in Data Science. The group consisted of 25 undergraduate faculty from a variety of institutions in the U.S., primarily from the disciplines of mathematics, statistics and computer science. These guidelines are meant to provide some structure for institutions planning for or revising a major in Data Science

Teaching programming with computational and informational thinking

Computers are the dominant technology of the early 21st century: pretty well all aspects of economic, social and personal life are now unthinkable without them. In turn, computer hardware is controlled by software, that is, codes written in programming languages. Programming, the construction of software, is thus a fundamental activity, in which millions of people are engaged worldwide, and the teaching of programming is long established in international secondary and higher education. Yet, going on 70 years after the first computers were built, there is no well-established pedagogy for teaching programming. There has certainly been no shortage of approaches. However, these have often been driven by fashion, an enthusiastic amateurism or a wish to follow best industrial practice, which, while appropriate for mature professionals, is poorly suited to novice programmers. Much of the difficulty lies in the very close relationship between problem solving and programming. Once a problem is well characterised it is relatively straightforward to realise a solution in software. However, teaching problem solving is, if anything, less well understood than teaching programming. Problem solving seems to be a creative, holistic, dialectical, multi-dimensional, iterative process. While there are well established techniques for analysing problems, arbitrary problems cannot be solved by rote, by mechanically applying techniques in some prescribed linear order. Furthermore, historically, approaches to teaching programming have failed to account for this complexity in problem solving, focusing strongly on programming itself and, if at all, only partially and superficially exploring problem solving. Recently, an integrated approach to problem solving and programming called Computational Thinking (CT) (Wing, 2006) has gained considerable currency. CT has the enormous advantage over prior approaches of strongly emphasising problem solving and of making explicit core techniques. Nonetheless, there is still a tendency to view CT as prescriptive rather than creative, engendering scholastic arguments about the nature and status of CT techniques. Programming at heart is concerned with processing information but many accounts of CT emphasise processing over information rather than seeing then as intimately related. In this paper, while acknowledging and building on the strengths of CT, I argue that understanding the form and structure of information should be primary in any pedagogy of programming

The computational turn: thinking about the digital humanities

No description supplie

A Data Science Course for Undergraduates: Thinking with Data

Data science is an emerging interdisciplinary field that combines elements of mathematics, statistics, computer science, and knowledge in a particular application domain for the purpose of extracting meaningful information from the increasingly sophisticated array of data available in many settings. These data tend to be non-traditional, in the sense that they are often live, large, complex, and/or messy. A first course in statistics at the undergraduate level typically introduces students with a variety of techniques to analyze small, neat, and clean data sets. However, whether they pursue more formal training in statistics or not, many of these students will end up working with data that is considerably more complex, and will need facility with statistical computing techniques. More importantly, these students require a framework for thinking structurally about data. We describe an undergraduate course in a liberal arts environment that provides students with the tools necessary to apply data science. The course emphasizes modern, practical, and useful skills that cover the full data analysis spectrum, from asking an interesting question to acquiring, managing, manipulating, processing, querying, analyzing, and visualizing data, as well communicating findings in written, graphical, and oral forms.Comment: 21 pages total including supplementary material

Pervasive Parallel And Distributed Computing In A Liberal Arts College Curriculum

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