Course Catalogue

Learning Sciences Theory (parts 1 and 2)

The field of Learning Sciences concerns two interrelated questions: How do people learn? How can we support learning? This two-semester long course will provide students an overview of major theoretical perspectives that have been employed to describe how learning works, and serves as an introduction to interpreting education as a means of designing learning environments. Through assigned readings, discussions and design tasks and projects, students are expected to become competent in understanding cognitive, embodied, and social perspectives on learning and learning environment design, with a focus on human interaction with novel learning technologies and authentic practices for learners. Exposure to seminal literature in the field is expected to lay the theoretical foundation for students to develop new lines of inquiry within the Learning Sciences. Examples of topics that will be covered in the course include:

• Frameworks and metaphors of learning
• Learning environments and design-based research
• Preparation for future learning
• Collaborative learning
• Conceptual change and misconceptions
• Motivation and affect
• Scaffolding
• Embodied cognition (including motor development and learning)
• Assessment
• Complexity
• Cognitive neuroscience
• Key debates, educational myths and critical perspectives in the Learning Sciences

Learning Sciences Methods (parts 1 and 2)

This two-semester long course aims at providing students with the practical knowledge and skill of conducting research within the learning sciences, specifically, collecting, processing, interpreting and analyzing empirical educational data, including different lenses through which to view the nature of inquiry in the field, research design and ethics, and an overview of quantitative, qualitative and mixed methods research. The course will explorethese methodological perspectives by focusing on applied conceptual aspects of actual datasets and experiments in the Learning Sciences. Students will be expected to work individually or collaboratively on weekly tasks (e.g., reading and discussing relevant literature, justifying research designs, performing data analyses) throughout the semester. By providing opportunities to analyze empirical educational data, the course will allow students to develop an appreciation for the breadth of methods that can be employed to understand and improve the process of learning. Examples of topics that will be covered in the course include:

• Carrying out design-based research
• Experimental and quasi-experimental designs
• Correlational and survey designs
• Inferential statistics, including advanced statistical methods (e.g., mediation and moderation, Bayesian analysis)
• Qualitative coding of qualitative and quantitative data, along with advanced qualitative methods (e.g., knowledge and interaction analysis)
• Computational methods (e.g., psychometrics, prediction and structured discovery with educational data, learning analytics)
• Educational neuroscience methods
• Meta-analysis