Ccsc 2015 panel
Download as PPTX, PDF•0 likes•609 views
The document provides an overview of computer science curricula across four liberal arts universities, detailing requirements for core and elective topics, particularly in mathematics and programming. It emphasizes the inclusion of parallel programming and interdisciplinary approaches to computing education, alongside recommendations for preparing students for federal employment in computer science. Additionally, it discusses curriculum modifications for integrating computational techniques with other disciplines and outlines specific course requirements for an interdisciplinary IT major.
Ccsc 2015 panel
- 2. Overview Represent 4 different small, liberal arts universities University of Mary Washington Randolph-Macon College Furman University Elon University
- 3. Terminology used in Report Topics are identified as either “Core” or “Elective” with the core further subdivided into “Tier-1” and “Tier-2.” • A curriculum should include all topics in the Tier-1 core and ensure that all students cover this material. • A curriculum should include all or almost all topics in the Tier-2 core and ensure that all students encounter the vast majority of this material. • A curriculum should include significant elective material: Covering only “Core” topics is insufficient for a complete curriculum.
- 4. Mathematics Requirements in Computer Science CS2013 only specifies mathematical requirements that are directly relevant for the large majority of all CS, for example, • elements of set theory, • logic, and • discrete probability, among others). These mathematics requirements are specified in the Body of Knowledge primarily in the Discrete Structures (DS) Knowledge Area.
- 5. Our Required Mathematics CPSC 125 (4 Credits) Designed to prepare beginning Computer Science majors for advanced study by emphasizing the components of Discrete Mathematics especially related to Computer Science. Topics include systems, logic, methods of proof, counting techniques, mathematical induction, sets, relations, functions, vectors, matrices, graphs and trees. http://www.allthemath.org/about/
- 6. Our Required Mathematics CPSC 326 (4 Credits) Prerequisites: CPSC 125A and 240. Covers structures and concepts relating to the underlying theory of computation and mathematical models of actual physical processes. Also covers a repertoire of advanced algorithms for data processing, and the asymptotic analysis of those algorithms to describe their running time and space requirements. Topics may include formal languages, automata theory, Turing machine, the halting problem, NP- completeness, searching and traversal algorithms, dynamic programming, compression algorithms, and random number generation http://cs.umw.edu/~finlayson/class/fall15/cpsc326/
- 10. Electives and Math UMW Courses/Minors Math discussed in course or minor Data Science Minor at UMW Numerical Analysis Probability and Statistics Graphics Linear Algebra Security Cryptography Data Mining Linear Algebra Statistics Calculus
- 11. Recommendation for Students Students considering a career with the federal government should be aware that the US Department of Operations and Personnel Management standards require a minimum of 15 credit hours of mathematics in order for employees to be classified as a “Computer Scientist.” Students interested in federal employment in this classification are encouraged to take MATH 121, MATH 122, MATH 200, and any additional MATH course numbered 300 or higher. A minor in Applied Mathematics would also provide appropriate preparation.
- 12. Increasing Parallel Programming Content Survey conducted to inform the CSC ’13 Two areas most identified to add to Body of Knowledge security parallel and distributed computing
- 13. CSC ‘13 Recommendation New Parallel and Distributed Computing (PD) knowledge area Five tier-1 hours and ten tier-2 hours, Approximately 13 lecture hours for ugrad CS degree
- 14. UMW’s old model Some coverage of PD topics in required operating systems course Elective course: Parallel Computing
- 15. UMW’s new model Maintain previous coverage Add 2-3 week module on parallel programming to OOA&D course (CS 2 for us) Required for all majors (CS major, CS minor, CIS major, GIS major)
- 16. Content of Parallelism Module Mostly shared memory model of parallelism Using materials developed by Dan Grossman at the University of Washington goals of parallelism, the need for communication and coordination, atomicity, data races, bad interleavings, and deadlock.
- 17. Adding Distributed Computing Randolph-Macon College restricts a major <= 42 credit hours, including those required from other disciplines. Program was lacking coverage of the core topics from networking, operating systems, and parallel computing.
- 18. Modifying Existing Unix Course Already required a course that introduces C/C++ programming and the use of UNIX systems. Revised to refocus the contents on systems programming with C/C++ and UNIX as the tools that will be used instead of being the main focus of the course
- 19. Modified Course Content Covers core topics from several of the knowledge areas that our program was previously lacking: the use of sockets and network applications (NC knowledge area); memory management, currency and threads (OS); an introduction to parallel algorithms and communication and coordination between applications (PD)
- 20. Integrate More With Other Disciplines “Big Tent” View of CS: As CS expands to include more cross- disciplinary work and new programs of the form “Computational Biology,” “Computational Engineering,” and “Computational X” are developed, it is important to embrace an outward-looking view that sees CS as a discipline actively seeking to work with and integrate into other disciplines. Work force: need to prepare students to work in a variety of potential professions in a rapidly changing field that reaches across disciplines. Graduates: need not only technical skills, but also the ability to combine these skills with domain knowledge gained by communicating with and learning from experts in diverse domains.
- 21. Computing across the Curriculum Thematic approach to the introductory course Second-level general education courses Interdisciplinary IT major with cognate discipline Interdisciplinary teaching
- 22. Computing across the Curriculum Thematic approach to the introductory course Second-level general education courses Interdisciplinary IT major with cognate discipline Interdisciplinary teaching
- 23. New Introduction to CS Course Goal: Introduction to the Computer Science discipline through the lens of an interdisciplinary problem1 Expose students to computing approaches to problem solving they would not normally see until after completing their core courses, while illustrating the application of computing to diverse fields. What do you need to understand about Computer Science to use computing in an interdisciplinary context? Survey of topics in Computer Science Audience: Entry point to major Introduction to CS for non-majors 1 Tartaro, A. & Cottingham, H. 2014. A Problem-based Survey Introduction to Computer Science for Majors and Non-majors. Journal of Computing Sciences in Colleges.
- 24. The “Rules” 1. Instructor chooses problem area. 2. Instructor chooses computer science topics. 3. Instructor teaches majors and nonmajors. 24
- 25. Example Problem Areas Business Analytics and Data Mining “The Power of Words” Social Media 25 (Images from npr.org)
- 26. Example Problem Areas “Secret Communication” Bioinformatics Creativity & Digital Media 26 (Images from npr.org)
- 27. Interdisciplinary Information Technology Major with a Cognate Discipline Required core courses: CSC-105: Introduction to Computer Science CSC-121: Introduction to Programming CSC-122: Data Structures and Algorithms Elective options: Two additional CSC courses numbered 200 or above, with at least one numbered 300 or above, to be approved at the time of declaration by the Dept. of CS chair Three additional courses numbered 200 or above, with at least one numbered 300 or above, selected from a cognate department or course of study (i.e. one leading to a major) to be approved at the time of declaration by the Dept. of CS chair, in consultation with the chair of the cognate department Capstone Experience (required): CSC-475: Seminar in Computer Science, or CSC-502: Research in Computer Science Synthesize student’s studies in CS and the cognate discipline
- 28. Relationship to Computer Science Designed to offer a complementary balance to our more theoretically oriented Computer Science major CS still the primary option for those students seeking graduate level degrees in Computer Science proper Common core of courses
- 29. Experience Majors declared with a variety of cognates thus far, including: Business Political Science Economics Music Religion Chemistry Biology Philosophy English Psychology Of 61 declared majors at present, 17 are IT (28%) In 2013, 10 of 45 majors were the “old” IT (22%)
- 30. No Changes validate some aspects of our existing curriculum Mobile Computing for the past five years (now explicitly covered as part of the new Platform-Based Development Knowledge Area) Math requirements in computer science reflected our current concerns and issues in developing students’ mathematical maturity within the quickly changing pre-requisite structure from our math department.
Editor's Notes
- #25: We have 3 fairly simple guidelines for instructors teaching this course, which is almost every instructor in the department: The instructor chooses a problem area The instructor chooses computer science topics, including some programming, and some advanced topics as they relate to the problem of the course. There are some topics, like AI or databases, that probably come up in multiple sections. Others like human computation or crowdsourcing, which I discuss in mine, may be more unique to the topic of the course. The instructor targets the material at majors in their first course (with or without previous experience) and non-majors.s
- #26: Some of our example problem areas include: Business analytics and data mining, which demonstrates the use of computer science to analyze and visualize complex information for use in business planning and decision making. The power of words explores how people and computers work together to solve a variety of problems involving text such as intelligent search or filtering structured information from unstructured text Social media examines how social media technologies are designed and implemented, focusing on the algorithms that make them work as well as societal implications of the technologies.
- #27: Secret communication looks at encryption by exploring how information is represented on a computer and manipulated into different forms. Bioinformatics explores computer science topics needed to solve complex genetics and molecular biology problem And creativity… looks at how computers can be used as tools for creative expression and entertainment.