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Computer Science: Graduate Programs

Overview: 

The Department offers the PhD and MS degrees in Computer Science. The MS degree includes the regular two-year program and a recently started Accelerated MS program, in which students can take 5 years to complete both BS and MS requirements.

The Master of Science (MS) program offers students strong academic preparation for work in industry and research. The program is classroom-based with opportunities for research participation. The MS degree has two options: the thesis option and the non-thesis option.

The Doctor of Philosophy (PhD) degree in Computer Science prepares the student for work in academia, industry and research. The graduate curriculum is well rounded and emphasizes the practical aspects of building useful software systems. We offer courses in traditional areas such as algorithms, programming languages, operating systems, distributed computing, networks, databases, artificial intelligence, and theory of computing.

The strength of the graduate program is found in the depth of our curriculum, the high quality of teaching and research supervision, and the stimulating environment that the Department strives to provide to its students in serving their needs. For example, the faculty encourages the students to serve as summer research interns and assists them in locating appropriate positions in reputable organizations. The high quality of teaching is reflected in our teaching evaluations, and the high quality of research supervision can be seen from the success of our graduate students. Our graduate students frequently publish papers in high quality conferences (CCS, SIGMOD, SIGCOMM, PODS, RECOMB, HPCA, INFOCOM, MOBIHOC, etc.). All students are treated equally in terms of facilities and services that are made available to them, and all doctoral students are assigned office space and a workstation and given access to supplies by the Department.

Expected Learning Outcomes: 

Expected Learning Outcomes of the Master of Science Program

The expected learning outcome are related to the three clusters of required courses, as appear in the  Core Curriculum. These three learning outcomes clearly describe what students should know and be able to do, and they address the flexibility built into the MS program by targeting the clusters of required courses.

The Computer Science Department expects each student in the MS program to master the two fundamental areas, and have additional expertise in combinations of these areas and additional specialized application areas.

Goal 1 (Foundations of Systems):  Being able to understand fundamental principles of computer systems, and implement components of such systems.  Measured mostly by success in one of the following courses:

·       525 Principles of Computer Networking

·       552 Advanced Operating Systems

·       553 Principles of Compilation

·       576 Computer Architecture

Measured Goals:

  1. Design, implement, test, and debug programs.
  2. Understand and appropriately apply multiple computing paradigms.
  3. Use software development tools.

Goal 2 (Foundations of Theory): Apply Mathematical Methodologies to Solve Computational Tasks. Measured mostly by success in one of the theory courses:   

·       545  Design and Analysis of Algorithms

·       573 Theory of Computation

Measured Goals (for CSc445)

a.      Model a variety of real-world problems using appropriate data structures.

b.     Select algorithms appropriate to particular purposes and apply them.

c.      Understand the central concepts and skills required to design, implement, and analyze algorithms for solving problems.

Goal 3 (Applications of Computing): Obtain some specialization in key application domains of computer science.   Measured mostly by the ability to develop, implement, prove or simulate ideas from one of the following courses:

·       520 Principles of Programming Languages

·       522 Parallel and Distributed Programming

·       533 Computer Graphics

·       547 Green Computing

·       560 Database Systems Implementation

·       566 Computer Security

·       550 Algorithms in Bioinformatics

·       537 Computational Geometry

·       577 Introduction to Computer Vision

Measured Goals:

a.      Have in-depth knowledge of, and ability to build, a large software system.

b.     Leverage existing software systems (e.g. simulator) in the development of new systems.

 

Assessment Map

The success of goal 1 will be measured by a proof of the ability to understand, implement or use simulators of a large system in one of the four fundamental systems areas.

The success of goal 2 will be measured by the following tasks:

a.     Model a variety of real-world problems using appropriate data structures.

b.     Select algorithms appropriate to particular purposes and apply them.

c.      Understand the central concepts and skills required to design, implement, and analyze algorithms for solving problems.

The success of goal 3 will be measured by the following tasks:  

a.     Have in-depth knowledge of, and ability to build, in one of the areas of expertise:

b.     Leverage existing software systems in the development of new systems.

 


Expected Learning Outcomes of the PhD Program


By completion of the PhD program in Computer Science, a student will reach the following goals:

  1. Master the critical ideas of computational thinking, and algorithmic approaches to problem solving.
  2. Demonstrate broad knowledge of his/her field.
  3. Critically analyze published research in his/her area.
  4. Conduct original research on a significant computer science problem.
  5. Effectively communicate and defended results of research to peers and broader scientific audiences, both in written and verbal formats. 

 

 

Assessment Activities: 

 


Activities for the Master Program


Direct Measures (general):

We will directly measure our success at achieving our learning outcomes using a combination of programming projects and exam questions.  For each, a scale of 1 through 4 will be used to indicate mastery: “unacceptable,” “marginal,” “good”, and “outstanding,” respectively.  We leave to the instructor of each course the tasks of mapping programming project, homework questions and test question grades to this 1 - 4 scale. The means by which each instructor rates the achievement of the learning outcome will be supplied along with the data during the Assessment Findings and Changes in Response to Findings phase.

The graduate affair committee would pick each semester 3 courses, one from each group, and would evaluate the success in meeting the goals in each of these courses.

This list of direct measures includes the name of the instructor and the semester in which the data will be gathered. The Academic Services and Student Support office will gather and aggregate this data and prepare a report for faculty to discuss the assessment results and any changes that appear warranted.

Direct Measures for Spring 2015

CSC 533 Computer Graphics

Provide an exam question and a project description indicating success in Goal 2 and Goal 3.

CSC 545  Design & Analysis of Algorithms

Provide an exam question(s) indicating success in Goal 2.

 

Indirect Measures

Anonymous survey that asks

·      Students’ perceptions of mastery of the 3 learning outcomes using a Likert scale

·      Availability and effectiveness of advising

·      What should the Computer Science degree program keep doing

·      What should the Computer Science degree program stop doing

·      What should the Computer Science degree program start doing

 

 


Activities for the PhD Program


 

The PhD program requires a student to complete a 5-semester portfolio, written qualifying exam, written and oral comprehensive exams based upon coursework and the student’s proposal for research, and a defense of the doctoral dissertation. These already-existing assessment activities are also used to gather program-level assessment data. 

 

 

 

 

 

Assessment Activity 

Outcome 1

Mastering Computational Thinking 

Outcome 2

Knowledge  of the field 

Outcome 3

Analysis of relevant literature 

Outcome 4

Conduct original research 

Outcome 5

Communication & Research Defense 

Portfolio 

X X

X

   

Written & Oral Comp. Exams 

X X X    

Written Dissertation & Oral Defense 

  X X X X

Exit Survey 

X X X X X

 

 

 

 

 

 

 

 

 

 

 

 


 

 

Comments:

1. The evaluation of the portfolio is done during a departmental meeting, by the department as a whole.

2. The other evaluations are performed by the corresponding committee members. 

 

The rubrics in Appendix A-D are used to assess all outcomes. The rubrics were developed by the faculty.

The chairs of the committees discussed below will submit the scored rubrics to the graduate program coordinator in the department. Once each year, at a late spring or early summer faculty meeting,  the summarized data are reviewed by the faculty.

The table of the Assessment Findings will show the summary that our faculty discussed after using the rubric for two years. 

 


Appendix A  

Assessment based on 5th semester Portfolio Evaluation of PhD student progress 

 

To be filled out by each member of the evaluating committee  - usually the

Graduate Affair/Curriculum Commitee 

 

Student Name:                       

Date:

Committee member name: 

 

Directions: Evaluate this student’s written portfolio,  and assign a score  between (Needs Improvement) and (Excellent) for each of the criteria described below. Below each score and statement, you could comment on the rationale of the score.

  • ____The student seems to master the fundamentals of computational thinking in her/his domain. (outcome #1) 

  • ____The student has identified the discipline of computer science he/she would like to work on, and is reasonably familiar with the fundamental results that are relevant to his/her research.  (outcome #2)

 

  • _____The student has demonstrated evidence of Research Ability/Achievement (outcome #3)

 

 


Appendix B  

  Assessment Activity: PhD Written and Oral Comp Exam. 

To be filled out by each committee member.

The Graduate Affairs Committee would use these results to assess whether outcome #1-#3 (and possibly outcome #4) are meet. 

 

Student Name:                 Date:

Committee member name: 

Directions: Evaluate this student’s written dissertation and oral defense of the research with a score between 1 (Needs Improvement) and 5 (Excellent) for each of the criteria described below. Below each score and statement, you could comment on the rationale if needed.   

  • ____  The student seems to master the fundamentals of computational thinking in her/his domain (outcome #1). 

  • ____ The student has demonstrated broad knowledge of the field(s) of Computer Science relevant to the proposed research (outcome #2). 

  • ____ The student has demonstrated critical thinking and analysis of the literature related to the proposed research,  and has a clear understanding of the potential contributions of the research work  (outcome #3). 

  • ____ The research design for studying the problem is appropriate. The algorithmic approaches are appropriate  (outcome #3+ possibly outcome #4). 

  • ____ (If relevant) The data from the experimental results are adequately analyzed. (outcome #3)

 

 

 


Appendix C

Assessment Activity: PhD Dissertation Defense

 

To be filled by each committee member.

The Graduate Affairs Committee will use these results to assess whether outcome #2-#5  have been met. 

 
 

Student Name:

Date of Exam:

Committee member name:

 

Directions: Evaluate this student’s written dissertation and oral defense of the research with a score between (Needs Improvement) and (Excellent) for each of the criteria described below. Below each score and statement, you could comment on the rationale if your score is less than 5. 

____The student demonstrated broad knowledge of related work  (outcome #2)

____The problem is clearly described with adequate critical analysis of the related research literature. (outcome #3)

____The research design for studying the problem is appropriate. (outcome #4)

____The Contributions to the CS knowledge are sufficiently significant. (outcome #4)

____The student demonstrates the ability to extrapolate his/her research to broader implications for the field. (outcome #5)

 

 


Appendix D

Online Exit Survey for Doctoral Students 

 

The Graduate Affairs Committee would use these results to assess whether outcome #1-#5  have been met. 

 

Please place an “X” in each row to indicate the degree to which your doctoral program provided you the opportunity to: 

 

 

Excellent 

Good 

Fair 

 Poor 

Master critical ideas of of computational thinking, and algorithmic approaches to problems solving. 

       

Acquire broad knowledge in my field.

       

Develop skills in critical analysis of research literature. 

       

Design and conduct original research.

       

Improve my ability to communicate my results to scientific peers. 

       

 

  1. Please describe learning opportunities for other specific skills or knowledge upon which this academic program should improve.

  2. How would you characterize the quality of advising and mentoring you received in this program?

  3. How well prepared do you feel for the academic job market?

  4. How well prepared do you feel for the industrial job market? 

 

 

 

Assessment Findings: 

Findings from Portfolie Evaluation 2015 (n=2)

 

Mastering computational thinking (outcome #1)

Identifying and familiarity with research domain
(outcome #2)

Demonstrated Evidence of Research Ability

(outcome #3)

Student 1 2 3 1

Student

5 5 5

 

Change in Response to Findings: 

TBA as more data is collected

Updated date: Sat, 05/09/2015 - 18:00