Curriculum & Course Schedules

LRN_SCI 403: Foundations of the Learning Sciences
Cognitive and social science theories of how people learn to understand, reason, and solve problems. Implications for the design of classroom learning environments; learning in real scenarios for investigating central issues in cognitive science. Learning in mathematics, science, reading/writing, and informal reasoning.

LS 402: Social Dimensions of Teaching and Learning
Students' relationships with one another and with teachers in school and nonschool settings. Implications for classroom instruction of social learning theory, student diversity, classroom climate, cooperative and competitive goal structures, and processes of attribution and achievement motivation.

LS 403: Foundations of the Learning Sciences
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LS 426: Design of Technological Tools for Thinking and Learning
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LS 404: Methods and Epistemologies for the Study of Learning I
This course focuses on the development of research questions and understanding the range of possible methodological approaches to understanding learning based on those questions.

LS 405: Methods and Epistemologies for the Study of Learning II
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LS 410: Quantitative Methods I
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LS 411: Quantitative Methods II (regression analysis)
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LS 451: Discourse Analysis
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LS 451: Interaction Analysis
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LS 415: Field Methods
 The purpose of this course is to introduce students to the world of qualitative research so that they will be able to read qualitative studies intelligently, and learn to design and conduct qualitatively oriented studies themselves. Beginning with an overview of the epistemological assumptions behind different kinds of research, the course will explore various types of qualitative research approaches and the kinds of topics and queries they support. Students will read and critique examples of published research of various kinds. Next, students will investigate the various methods of collecting qualitative data.  The class is designed so that students simultaneously read about and discuss qualitative research, and gather data themselves.  Although the course touches on analysis, the main focus is on developing a qualitative research project and collecting data for it.

LS 416: Advanced Qualitative Methods
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LS 451: Special Topics in Computational Methods
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CS 472 / LS 451: Designing and Constructing Models with Multi-Agent Languages
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CS 497: Advanced multimodal learning analytics
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Students will declare a Computer Science doctoral degree track (e.g., Graphics and Interactive Media or AI/ML) as outlined in the Computer Science graduate study manual (section 4). Students should take at least 5 courses in CS that are approved for graduate credit (all 300 and 400-level courses). In general, we require a breadth of experience in all of the following areas:

Programming (comparable to CS 111+211+214)

Human Computer Interaction

AI, Machine Learning, or Cognitive Systems

Systems (1 of the following)

• Operating systems 
• Databases 
• Computer architecture 
• Networking 
• Programming languages 
• Computer Graphics 

Theory (1 of the following)

• Fundamental algorithms 
• Computing and complexity theory 

3 courses between year 2 and 3
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. Students should consult with their advisor to select courses meaningful to their interests.

GEN_ENG 519
Students must complete the McCormick Responsible Conduct of Research training course: GEN_ENG 519. More information is available here.