This course will focus on understanding motivation theory and research, and then applying these understandings to our lives in two main ways. First, we'll develop a deeper awareness of our own motivations, including personal strengths, obstacles, and opportunities for growth. Second, we'll apply our insights to help build more motivationally-supportive environments (e.g., school, sport, work, family, community).

This course gives students conceptual and philosophical frameworks for leading good instruction and creating conditions for teaching and learning in schools and districts. Students reflect on their own instructional filters and deepen understanding of what we know about effective teaching and learning. Considering various instructional issues, students learn to supervise and evaluate instruction, connecting supervision with professional growth.

Psychological and social development during adolescence are affected by multiple factors, such as biological, social and cultural changes, and larger macrosystem influences. We will examine how these influences shape development generally during the 2nd and 3rd decades of life. We will explore questions of identity, relationships, health and culture by considering key questions that adolescents explore such as "Who am I," and "Where am I going?"

The intersection of evaluation theory and practice is focused on with an emphasis on the design of thoughtful, ethical evaluation inquiry about problems of practice (POPs) associated with educational programs. The course focuses specifically on developing the knowledge, skills, and understandings regarding evaluation and the collection of information to make judgments about an education initiative or program.

An overview of current program evaluation approaches, this class is designed to provide an overview of the theories behind and approaches to evaluation as well as to begin to train students in evaluation design and methods. Theoretical, methodological, and empirical readings emphasize the terminology of educational evaluation and the variety of theoretical and design approaches to evaluation. Consideration is also given to the application of evaluation approaches and designs to non-educational settings.

Examines how, as a team, school leaders analyze the relationship of the integration of technologies to teachers' beliefs & practices & to local school culture, structures, & policies. Students will evaluate factors critical for successful implementation of educational technology; identify & deconstruct the distributed leadership of educational technology in a school setting; & plan comprehensively for a system of practice to lead EdTech.

Through research based practices and theoretical framing, students learn to build schools' professional capacity through recruitment, interviewing, induction, professional development, evaluation, and compensation. The process is considered in school and policy contexts with attention to ethical and diversity considerations. Students develop actionable plans for employing, supporting, and retaining professional capacity as a leader.

This course covers the basic principles of engaging families and the community in the life of a school, a fundamental responsibility of school leaders. Topics include communication with the school community and families, community partnerships, crisis communications, and research-based engagement practices. This course will provide tools and resources for building positive relationships with staff, parents, and the community at large.

This course is designed to familiarize students with the basics of multilevel modeling. Topics include random effects ANOVA models, means-as-outcomes models, random coefficients models, intercepts- and slopes-as-outcomes models, contextual models, random effects ANCOVA models, linear growth models, nonlinear growth models and cross-classified models. Prerequisite: EDLF 7420 or equivalent.

Focus is on the generalized linear model (GLM) for cases when variables have specific non-normal conditional distributions, with emphasis on common data analytic challenges that arise in real world settings. Topics include nonlinear relationships, nominal and ordinal outcomes, discrepant data, and bootstrapping methods. Course materials are grounded in applied examples from the social and health sciences.