Learning Sciences and Technologies

PSY 501. FOUNDATIONS OF THE LEARNING SCIENCES

This course covers readings that represent the foundation of the learning sciences, including: Foundations (Constructivism, Cognitive Apprenticeship, & Situated Learning); Approaches (Project-based Learning, Model-based reasoning, Cognitive Tutors); and Scaling up educational interventions. The goal of this course is for students to develop an understanding of the foundations and approaches to the Learning Sciences so that they can both critically read current literature, as well as build on it in their own research. (Prerequisites: None)

PSY 502. LEARNING ENVIRONMENTS IN EDUCATION

In this class, students will read and review both classic and critical current journal articles about learning technologies developed in the Learning Sciences. This course is designed to educate students on current technological approaches to curricular design, implementation, and research in the Learning Sciences. (Prerequisites: None)

PSY 504. META-COGNITION, MOTIVATION, AND AFFECT

This course covers three key types of constructs that significantly impact learning and performance in real-world settings, including but not limited to educational settings. Students will gain an understanding of the main theoretical frameworks, and major empirical results, that relate individuals? meta-cognition, motivation, and affect to real-world outcomes, both in educational settings and other areas of life. Students will learn how theories and findings in these domains can be concretely used to improve instruction and performance, and complete final projects that require applying research in these areas to real-world problems. Students will do critical readings on research on this topic. (Prerequisites: None)

PSY 505. ADV MTHD & ANALY FR LRNG & SS

This course covers advanced methods and analysis for the learning and social sciences, focusing on contemporary modeling and inference methods for the types of data generated in these forms of research. This course will enable students to choose, utilize, and make inferences from analytical metrics that are appropriate and/or characteristic to these domains, properly accounting for the characteristic forms of structure found in data typically collected for research in the learning and social sciences. Some of the topics covered will include ROC analysis and the use of A? for assessing student models, learning curve and learning factor analysis, social network and dyad analysis, and appropriate methods for tracking student learning and behavior in longitudinal data. Readings will be drawn from original source materials (e.g. journal articles and academic book chapters). (Prerequisites: PSY 503, Research Methods for the Learning Sciences, comparable course, or instructor discretion.)

 
  • Email a Friend
  • Bookmark this Page
  • Share this Page