Master of Science in Applied Computer Science

Transcription

1 Learning Assurance Report For the Master of Science in Applied Computer Science Department of Computer Science and Information Systems College of Science and Mathematics Fall 2003 Prepared by Ken Hoganson, Ph.D.

2 Learning Assurance Report For the Master of Science in Applied Computer Science Fall 2003, Ken Hoganson Brief Program Overview The Master of Science in Applied Computer Science (MSACS) at Kennesaw State University is housed in the Department of Computer Science and Information Systems within the College of Science and Mathematics. The MSACS offered its first set of courses in Fall 2002, with its first graduates anticipated in Spring of We currently have 37 active graduate students in the program. The MSACS is a non-traditional, premium-priced program ($5000 per semester, $25,000 total), for experienced professionals. The MSACS prepares students for employment as software architects, embedded systems engineers, software engineers, client-server systems designers and programmers, and any other computing career requiring advanced knowledge of computer science. The Master of Science with major in Applied Computer Science for Experienced Professionals (MSACS) is a thirty-six hour graduate degree program with coursework in the following areas: Embedded Systems Computing Systems Software Engineering Principles Software Architecture Parallel & Distributed Systems Distributed Object Technology Database Administration A.I. and Robotics The MSACS is designed for the working professional in computing or information technology interested in obtaining a graduate degree in computer science, to be pursued concurrently with work commitments. Courses are offered in a resource-efficient cohort/learningcommunity allowing students to complete the program in 22 months (5 semesters including a summer semester). Distance learning technologies are integrated in instruction delivery. The Fall-Cohort program is approachable for students with modest technical backgrounds, and does not require an undergraduate degree in computer science. Prerequisite foundations for admission include mathematics, physical science, computer programming, computer hardware, and elementary data structures. Work experience can selectively replace certain prerequisite knowledge foundations at the discretion of the program director with demonstration of student competency in the knowledge area. A set of self-study online courses is available to strengthen foundations of matriculating students. The target student 2

3 audience includes both students concentrating on completing their Master s degree, and students completing the degree concurrent with work commitments. Web-Based Instruction Delivery Instruction is delivered both on campus and at a distance, using web-based technologies, allowing students to attend lectures on campus, or remote and live from work or home. All lectures are also recorded allowing students to view the archived lecture at their convenience. Real-time interaction between the students and professor will be supported using Voice-over-IP and online "chat". 3

4 General (GLO) and Specific Student Learning Outcomes (SLO) of the MSACS Program The following student learning outcomes are derived from the educational objectives listed above for the Master of Science in Applied Computer Science program: K,S,A 1. General Learning Outcome. Understanding the fundamental concepts of computer science. (K) Specific SLOs: 1.1. Students will be able to demonstrate their understanding of the fundamental concepts of computer science through programming, analysis, design, and performance analysis. (K) 1.2. Students will be able to create and build data structures and build object-oriented systems. (K,S) 1.3. Students will analyze algorithms and their efficiency. (K) 2. General Learning Outcome. Understanding Software Engineering Principles and Practice. (K, S) Specific SLOs: 2.1. Students will be able to demonstrate their understanding of modern software engineering. (K,S) 2.2. Students will apply their understanding of modern software engineering through analysis, design, construction, and evaluation of software projects. (K,S) 2.3. Students will apply their understanding of object-oriented design through analysis, design, construction, and evaluation of software projects. (K,S) 3. General Learning Outcome. Understanding computer hardware organization and architecture. (K, S) Specific SLOs: 3.1. Students will apply their understanding of computer hardware organization and architecture through performance evaluation and bottleneck analysis. (K) 3.2. Students will be able to solve problems in computer organization and architecture utilizing design principles and complex problem solving. (K, S) 4. General Learning Outcome. Understanding Embedded Systems Architecture, Organization, and Design. (K, S) Specific SLOs: 4.1. Students will be apply their understanding of embedded systems through constructing machine language programming on microcontrollers. (K, S) 4.2. Students will apply their understanding of embedded systems by applying the software development process to create embedded systems. (K, S) 4.3. Students will demonstrate their understanding of embedded systems real-time programming, and real-time design principles. (K, S) 4

5 5. General Learning Outcome. Independent Applied or Theoretical Research. (K, S, A) Specific SLOs: 5.1. Students will do either an applied project or a theoretical research project in computer science under the direction of a faculty research director. (K, S, A) 5.2. Students will demonstrate and apply their ability to work independently through completing an applied project under the direction of a faculty research director. (S, A) 5.3. Students will write a research paper summarizing the methodology and results of their research project. (S, A) 6. General Learning Outcome. Understanding Parallel Processing and High- Performance Computing. (K, S) Specific SLOs: 6.1. Students will analyze case studies in parallel processing and high-performance computing. (K) 6.2. Students will conduct and evaluation of parallel processing and high-performance computing through performance analysis of real and theoretical machines, and appraisal of the results and their implications. (K) 6.3. Students will demonstrate their understanding of parallel processing and highperformance computing through the theoretical and engineering laws and limitations that govern the performance of computing machines. (K, S) 7. General Learning Outcome. Understanding Database Theory and Practice. (K, S) Specific SLOs: 7.1. Students will apply their understanding of database administration by completing class projects requiring analysis, design, construction, and evaluation of the final product. (K, S) 7.2. Students will demonstrate their understanding of the theoretical foundations of database. (K) 7.3. Students will apply their understanding of database administration and theory through the construction and evaluation of properly designed database systems. 8. General Learning Outcome. Understanding Distributed Computing Theory and Practice. (K, S) Specific SLOs: 8.1. Students will complete class projects and assignments requiring analysis, design, construction, and evaluation of software projects. 9. General Learning Outcome. Understanding Professional and Ethical Standards. (A) Specific SLOs: 9.1. Students will demonstrate their understanding of professional and ethical standards, and their implications for business practices, and software and system design issues where safety and security are concerned. (A) 5

6 10. General Learning Outcome. Understanding Application and Software Architecture. (K) Specific SLOs: Students will demonstrate their understanding of software architecture. (K) Students will evaluate application architectures and alternative design approaches. (K) Students will apply application architecture principles through analysis, design, development and evaluation of software architecture projects. (K) 6

7 Matrix of Specific Learning Outcomes Related to MSACS Courses Specific Learning Outcome 1. Fundamental Concepts of Computer Science 1.1 X X X X X X X 1.2 X X X X 1.3 X X X X X 2. Software Engineering Principles and Practice 2.1 X X X X 2.2 X X 2.3 X X X X X X 3. Computer Organization and Architecture 3.1 X X X 3.2 X X 4. Embedded Systems Architecture, Organization and Design 4.1 X X X 4.2 X X X 4.3 X X 5. Independent Applied or Theoretical Research 5.1 X 5.2 X 5.3 X 6. Parallel Processing and High-Performance Computing 6.1 X 6.2 X 6.3 X 7. Database Theory and Practice 7.1 X X 7.2 X 7.3 X 8. Distributed Computing Theory and Practice 8.1 X X 9. Professional and Ethical Standards 9.1 X 10. Application and Software Architecture 10.1 X X X 10.2 X X X X X X X 10.3 X X 7

8 Curriculum Overview The Master of Science with major in Applied Computer Science for Experienced Professionals (MSACS) is a thirty-six hour graduate degree program with a premium tuition ($5000 per semester) and a rigid cohort-structure. Coursework is offered in the following areas: Embedded Systems Computing Systems Software Engineering Principles Software Architecture Parallel & Distributed Systems Distributed Object Technology Database Administration A.I. and Robotics The Fall-Start program is approachable for students with modest technical backgrounds, and does not require an undergraduate degree in computer science. The Spring-Start program DOES require a degree in a Computer Science or related discipline. Prerequisite foundations for admission include mathematics, physical science, computer programming, computer hardware, and elementary data structures or a computing undergraduate degree. Work experience can selectively replace certain prerequisite knowledge foundations at the discretion of the program director with demonstration of student competency in the knowledge area. Through their choice of 9 hours of electives, students may informally choose to concentrate in one of two areas: Application Architecture, or Embedded Systems and Robotics, or may choose from both areas. These two areas of concentration represent technology knowledge areas less amenable to the off-shore movement of technology jobs, based on surveys and analysis by quoted experts. The MSACS is evolving to place greater emphasis on these two areas. Software architecture plays to our faculty s strengths and the MSACS program s emphasis in software engineering, while the recent addition of Robotics and Artificial Intelligence builds on our relatively unique concentration in embedded systems. Both areas are seen as highdemand and high-growth areas where technology (and jobs) are likely to evolve rapidly. Both areas are regarded as likely to remain somewhat insulated from the off-shore movement of technology jobs phenomena, which is gaining momentum and affecting entrylevel and basic programming employment. MSACS students will be able to choose to concentrate in either area, or remain general applied CS. The informal concentrations are achieved by the choice of one of two elective courses, the choice of the professional conference attendance, and by optionally directing an applied project into one area or the other. Thus, up to 9 graduate hours may be concentrated in either Applications Architecture or Embedded Systems/Robotics. The Concentrations: Nine hours of study may be optionally directed by the student into one of two areas of concentration, or may be completed as general degree requirements without a concentration. The two areas of concentration are Embedded Systems, and Software Architecture both of which stand on the shared foundation of coursework in computing systems, embedded systems, and software engineering. Students who choose to concentrate on embedded systems will attend the Embedded Systems Conference and take CS8650 Introduction to AI and Robotics, and may choose to complete the UNIX 8

9 certification or complete a research project. Students who choose to concentrate in Software Architecture will attend the Joint International Conference and take CS8625 Software Architecture, and may choose to complete the ORACLE certification or complete a research project. Embedded Systems & Robotics Concentration CS 8650 Introduction to AI and Robotics Embedded Systems Conference Embedded Project Building on required coursework in: Computing Systems Sequence (6 hours) Embedded Systems Sequence (6 hours) Software Engineering Sequence (9 hours) Software Architecture Concentration CS 8628 Software Architecture Joint International Conference Software Architecture Project Building on required coursework in: Software Engineering Sequence (9 hours) Database Administration (3 hours) Distributed Technology (3 hours) Master of Science in Applied Computer Science Curriculum: New Catalog description CORE REQUIRED TRADITIONAL COURSEWORK: CS 8531 Software Engineering 3 CS 8532 Advanced Software Engineering 3 CS 8411 Embedded Systems and Micro-controllers 3 CS 8512 Advanced Embedded Systems and Micro-controllers 3 CS 8625 High-Performance Computing 3 CS 8630 Database Administration 3 Credit Hours 18 hours COHORT ADMISSION SPECIFIC COURSES: 9 hours Fall Admission Program Requirements: (9 hours) CS 8421 Computing Systems 3 CS 8422 Advanced Computing Systems 3 CS 8430 Object-Oriented Software Design 3 OR Spring Admission Program Requirements: (9 hours) CS 8990 Special Topics (taken two or more times) 6 CS 8635 Distributed Object Technology 3 ELECTIVES 9 hours Elective Coursework (3 hours required): Choose one of: CS 8628 Software Architecture 3 CS 8650 Introduction to AI and Robotics 3 Applied Studies Electives (6 hours required): Students may elect to work on a professional certification program in UNIX or ORACLE, or students may choose to design an applied research project working closely with a faculty sponsor, or may choose to attend a professional conferences and write a research paper. Students must complete six hours of study in applied computer science topics: CS 8940 Directed Study: Professional Conference 3 CS 8940 Directed Study: Applied Research (3 or 6 hours) 3-6 CS 8940 Directed Study: Professional Certification 3 An applied research project requires a formal project proposal and plan, and must be approved by a faculty sponsor and by the program director. 9

10 MSACS Course Sequence (and prerequisites and Plan of Study) The following tables show the courses offered each semester starting Fall 2004 for the MSACS, which is also the student s plan-of-study in the fixed-schedule cohort program. The schedule that begins in Fall 2004 is the fall admission schedule, while the schedule that begins in Spring 2005 is the Spring admission schedule. The two admission schedules share a large body of overlapped coursework and applied studies. The differences are primarily in the first semester of the Fall admission (Fall 2004), and the last semester of the Spring admission (Summer 2006). Fall Admission Schedule (with or without degree in computing) F04 SP05 SU05 F05 SP06 SU06 CS8421 Computing Systems CS8430 Analysis & Design CS8422 Computing Systems CS8411 Embedded Sys CS8625 High-Perf Comp CS8512 Adv Embedded Sys CS8630 Database CS8431 Software Eng CS8940 Joint Int l Conf. CS8628 S.W. Architecture CS8532 Adv. Software Eng. CS8650 A.I & Robotics CS8940 Emb. Sys. Conf. CS8940 Applied Project or Cert Part-Time: CS8990 Special Topics CS8990 Special Topics Full-Time: Spring Admission Schedule (degree in computing required for Spring admission) F04 SP05 SU05 F05 SP06 SU06 With CS degree CS8990 Special Topics CS8411 Embedded Sys CS8625 High-Perf Comp CS8512 Adv Embedded Sys CS8630 Database CS8431 Software Eng CS8940 Joint Int l Conf. CS8628 S.W. Architecture CS8532 Adv. Software Eng. CS8650 A.I & Robotics CS8990 Special Topics CS8635 Distributed Systems CS8940 Emb. Sys. Conf. CS8940 Applied Project or Cert Part-Time: CS8422 Computing Systems Full-Time:

11 Self Evaluation SECTION I- ARTICULATING STUDENT LEARNING OUTCOMES I. Overall Summary of the Strength of the Student Learning Outcomes Rating: Strong The student learning outcomes reflect the appropriate knowledge, skill, and attitude balance for a math-based science. The SLOs at the specific level are differentiated from the general SLOs. There are no standards for graduate computer science programs, but the SLOs are consistent with the common and accepted model for computer science. The SLOs are strong in knowledge, and as is common in the math-based sciences, build on prior learning both the knowledge foundation required for admission, and the knowledge building within the fives semester of the program. The SLOs reflect both lower-level and higher-level thinking. IA. IB. Knowledge/Skill/Attitude Balance and Student Centered Rating: Appropriate The student learning outcomes reflect the appropriate knowledge, skill, and attitude balance for a math-based science. Most SLOs are knowledgecentered, with appropriate skills in applying that theoretical knowledge using a technology tool. Only a few SLOs are Attitude-oriented, those reflecting the professional standards associated with the practice of Software Engineering in discussions of ethics and the discipline (SLO 9.1), and those related to teaching scientific research and methods (SLO 5.1, 5.2, 5.3). Ethical considerations are associated with the practice of Software Engineering, in recognizing that as in any engineering area where human lives depend on the proper functioning of the created device (bridges, aircraft), software correctness proofs and validation are important (example: the software running the hardware in the modern fly-by-wire aircraft). SLO Differentiation Rating: Excellent The specific student learning outcomes are focused and measure the general student learning outcomes to which they are associated. The specific student learning outcomes use action verbs and are measurable. For example, General Learning Outcome 4.0 Understanding Embedded Systems Architecture, Organization, and Design. (K,S) is associated with specific SLO 4.1: Students will be able to demonstrate and apply their understanding of embedded systems through machine language programming in microcontrollers. (K, S). The GLO specifies the knowledge area domain, while the SLO describes a specific, measurable, and demonstrable portion of that knowledge area and a related skill. IC. Compliance with Disciplinary Conventions 11

12 Rating: Satisfactory There are no standards for graduate computer science programs, but the SLOs are consistent with the common and accepted model for computer science. ID. IE. Building Upon Prior Learning Rating: Exemplary All courses are taken in a lock-step cohort program, so that each course has a clearly defined set of prerequisites (all the previous courses) that ALL students must complete in the same sequence. Some courses will of course, be stronger and more specific foundations for follow-on courses, for instance, the two course sequence in computing systems. Lower-Order and Higher-Order Thinking Rating and Response: Exemplary General Learning Outcomes tend to focus on a broad category of knowledge area and higher-order thinking about that knowledge area, while SLOs address specific lower-level thinking and specific skills. For instance, GLO 4. encompasses a broad knowledge area where the overall understanding of the issues involves is a higher-order thinking. The SLOs under GLO 4. describe specific knowledge and skills that the student will obtain and demonstrate. 12