This course gives students experience in significant practices in educational testing/evaluation. Topics covered include computer-based testing, assessment policy, new directions in evaluation and validation, and how to write research reports.

Description not available at this time.

Description not available at this time

Design, Visual Thinking & Technology: Fundamentals of Visualization for Millennials offers first-year students the opportunity to learn the basics of design, visual thinking and the technology applications-Google Drive, Prezi, Storify, Pinterest, Tableau Public, and the Advanced Features of MS PowerPoint. Topics include principles of design, layout, typography, color, and data visualization. In this seminar, students will work on both individual and collaborative assignments in a computer lab and become familiar with technology applications in the completion of these assignments.

New technologies and online tools are transforming learning and empowering learners. In this course, we will examine
the latest advances in the field of educational technology and discuss how these advances will redefine the future of K--?
12 and higher education. Topics covered include: 3D Printing, Maker Movement, Gamification, Wearable Technology, Ubiquitous Computing, Massive Open Online Courses (MOOCs), Artificial Intelligence, Mobile Learning, and Connected Learning. We will also critically examine the relationship between technology, access, knowledge, and power.

Workshop addresses the dynamics of classism on personal and institutional levels.

Workshop addresses the dynamics of racism on personal and institutional levels.

Not available at this time.

Satisfies Junior Year Writing requirement for Biology majors. Students write and revise short papers on subjects likely to be encountered by biologists. Class discussion of papers. Prerequisites: 3 biological science courses, for declared Biology majors only.

First semester of a two-semester sequence. Emphasis given to probability theory necessary for application to and understanding of statistical inference. Probability models, sample spaces, conditional probability, independence. Random variables, expectation, variance, and various discrete and continuous probability distributions. Sampling distributions, the Central Limit Theorem and normal approximations. Multivariate calculus introduced as needed. Prerequisites: MATH 132, or 136. (Gen.Ed. R2)