Computer Science and Learning Sciences PhD

The following requirements are in addition to, or further elaborate upon, those requirements outlined in The Graduate School's Policy Guide.

Course Requirements

Students are expected to take courses during the first two years of their graduate career. Every student is required to take courses that fulfill specific requirements for breadth and depth in computer science and learning sciences. Students are also expected to take coursework and continue reading beyond these specific requirements. In particular, students should take coursework that is relevant to their research.

Learning Sciences Foundational Courses (4 courses)

• LS 401 ---OR--- COMP_SCI 371: Knowledge representations
• LS 402: Social Dimensions of Teaching and Learning
• LS 403: Foundations of the Learning Sciences
• One Learning Sciences Design Course:
• LS 426: Design of Technological Tools for Thinking and Learning ---OR---
• LS 451 / COMP_SCI 313, 314: Tangible Interaction Design and Learning

Learning Sciences Approved Methods Courses (choose 3 courses)

• LS 410: Quantitative Methods I
• LS 451: Quantitative Methods II (regression analysis)
• LS 451: Discourse Analysis
• LS 415: Field Methods
• LS 416: Advanced Qualitative Methods
• LS 451: Computational Methods
• COMP_SCI 472 / LS 451: Designing and Constructing Models with Multi-Agent Languages

Computer Science Foundational Courses (at least 5 courses)

Students will declare a Computer Science concentration (e.g., Graphics and Interactive Media or Cognitive Systems). Students should take at least 5 courses in CS that are approved for graduate credit (all 300 and 400-level courses, unless specifically listed as ineligible for graduate credit). Students should consult the qualifying procedures for their program to ensure they have the necessary background for their concentration. The requirements for GIM and CogSys are listed below for reference:

All GIM students are required to demonstrate proficiency in computer science and other core fields of GIM:

• Programming (comparable to CS 111+211+311)
• Theory
  • Fundamental algorithms
  • Computing and complexity theory
• Systems (2 of the following)
  • Operating systems
  • Computer architecture
  • Networking
  • Programming languages
• Graphics or media
• Cognitive and social systems (any course in AI, cognitive science, social science)

By the Qualifying Exam, you should be conversant with the material in the following courses:

• COMP_SCI 325: Artificial Intelligence Programming
• COMP_SCI 337: Semantic Information Processing
• COMP_SCI 338: Practicum in Intelligent Information Systems
• COMP_SCI 344: Design of Computer Problem Solvers
• COMP_SCI 348: Introduction to Artificial Intelligence
• COMP_SCI 349: Machine Learning
• COMP_SCI 371: Knowledge Representation

Breadth Courses (3 courses)

Three additional courses are required within years 2 and 3. Any non-required, graduate-level course in any school or department can be used to fulfill the breadth requirement.

Other Degree Requirements

• Second-year qualifying exams
• Second-year independent research project
• PhD Dissertation