Psychology

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.

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.

This course covers research methods used in the Learning Sciences. Students will gain expertise and understanding of think-aloud studies, cognitive task analysis, quantitative and qualitative field observations, log file analysis, psychometric, cognitive, and machine-learning based modeling, the automated administration of measures by computer, and issues of validity, reliability, and statistical inference specific to these methods. Students will learn how and when to apply a variety of methods relevant to formative, performance, and summative assessment in both laboratory and field settings. Readings will be drawn primarily from original source materials (e.g. journal articles and academic book chapters), in combination with relevant textbook chapters.

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 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.

This course will cover selected topics related to learning and creativity— including measurement, memory, semantic networks, sleep, analogies, problem-solving, divergent thinking, and insight moments. Students will critically review journal articles and other forms of media to gain a better understanding of the processes involved in learning and creative cognition. Students will also learn about prominent theories of learning and creativity and identify ways to utilize these frameworks to improve education and student experiences in the classroom.

The purpose of this course is to examine current issues in learning sciences and education and introduce students to the analysis of nested data structures (e.g., students within classrooms). Longitudinal or repeated measures data can also be thought of as clustered data with measurement occasions nested within subjects. This course will focus on understanding the hierarchical (generalized) linear models and their assumptions, as well as practical aspects of developing models to address research questions and interpreting the findings. This course emphasizes practical, hands-on development, analysis and interpretation of hierarchical linear models. Readings will be drawn from book chapters on multilevel modeling and journal articles that utilize national longitudinal data sets to answer questions about student learning. The lab portion of this course will provide students with opportunities to learn and apply hierarchical linear modeling, mediation, and moderation to longitudinal data using two computer programs (HLM and SPSS). Students who received credit for SS 590: Applied Multi-Level Modeling in 2018 or 2015 cannot also take PSY 507 for credit.

(1-3 credits) This course provides an opportunity for graduate students to learn about a special topic within Psychological Science. This course may be repeated for different topics.

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.

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.

This course covers research methods used in the Learning Sciences. Students will gain expertise and understanding of think-aloud studies, cognitive task analysis, quantitative and qualitative field observations, log file analysis, psychometric, cognitive, and machine-learning based modeling, the automated administration of measures by computer, and issues of validity, reliability, and statistical inference specific to these methods. Students will learn how and when to apply a variety of methods relevant to formative, performance, and summative assessment in both laboratory and field settings. Readings will be drawn primarily from original source materials (e.g. journal articles and academic book chapters), in combination with relevant textbook chapters.

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 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.