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Career Exploration In Leadership For Community Education & Learning

This course is designed to help LCE students answer the questions: What will I do with my life after graduation? and How do I make the transition into a career? To answer these questions, students will visit multiple field experience sites, analyze activities and components of the field experience, analyze the professional field, and develop a post-graduation transition plan.

Senior Seminar In Leadership For Community Education & Learning

This course is designed as a capstone course for students within the Leadership for Community Education and Learning (LCE) major. This course will not only allow you to integrate the skills and knowledge you have acquired during your academic career in LCE, but also hone your skills in critical analysis and thinking. Ultimately, EDL 475 will allow you to focus on preparing yourself for graduation and a career, or graduate or professional school.

Professional Internship In Leadership For Community Education & Learning

This is a cooperative educational program between Leadership for Community Education & Learning (LCE) majors at the University of Kentucky and approved community organizations that collaborate with senior-level students within a chosen professional field. Students will work with their organizational placements to develop a "focused" professional project to be completed throughout the semester.

History, Development, And Equity In Shaping Early Childhood Policy

EDL 580 is designed to help students understand the importance of early development, the critical need to develop early childhood education and care (ECEC) policies grounded in research and practice, and the effectiveness and limitations of ECEC policies to address fundamental issues of inequality. Finally, the course is intended to enable students to place their own professional interests and concerns in a broader historical and educational context.

Using A Policy Framework To Examine Early Childhood Services And Issues

EDL 581 focuses on the practice of early childhood education and care (ECEC), as well as the role of families and policymakers in altering practices and policies. The course offers an analysis of the critical forces that have shaped contemporary ECEC, current ECEC services and policies, and inventive theories about how to organize such services to achieve optimal quality, equity, sustainability, and efficiency. Course sessions will confront diverse challenges that characterize the ECEC field and unveil some of the thinking and efforts to redress them.

Policy Research In Early Childhood

EDL 582 is focused using theory as a lens through which to understand the central questions, logic, and values that underpin how policy is understood and supported by analysts and researchers in the field of early childhood education and care (ECEC). Throughout the course, theories and policy tools will be applied to real-world examples, providing concrete opportunities to examine different, sometimes competing, analytic approaches and the contexts in which they are applied.

Graduate Student Professional Seminar

This course is required for all entering doctoral students in Educational Psychology, School Psychology, Counseling Psychology, and Quantitative and Psychometric Methods. The fall seminar serves as an orientation to the doctoral programs and as a support for graduate student success. Specifically, this course introduces some general expectations, guidelines, and strategies for successfully completing the PhD at a research institution and provides support for self-reflection and initial engagement with a research topic of interest.

Process Monitoring And Machine Learning

This course will include two major parts: machine learning theories and applications. Machine learning theories will cover legacy techniques (e.g., support vector machine, Bayesian inference) and then go deeper into deep learning (convolutional and recurrent neural network). The application part will cover some practical studies on how can we leverage the machine learning techniques to analyze the data collected from factory floors. Also, programming of the machine learning techniques (e.g., Python) will be covered in the class as well.

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