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The Louis Stokes Alliances for Minority Participation (LSAMP) program of the US National Science Foundation has been a primary force for raising the success and graduation of minority students in STEM for 30 years. Increasing the number of underrepresented students earning baccalaureate degrees, and entering graduate school in STEM is the goal of LSAMP. This goal has been nearly achieved through the formation of alliances of degree granting institutions of higher learning, varying from community colleges to major research institutions. Currently there are 59 alliances including more than 400 institutions. LSAMP is responsible for more than 650,000 bachelor’s degrees earned by minority students in STEM. The papers for this Research Topic should focus on the use of LSAMP activities, programs and collaborations to develop pathways to success and graduation of STEM majors from minority groups that underrepresented in STEM. These pathways can include any segment from pre-college through graduate school. Areas of special interest include mentoring, research experiences, transitions between levels and novel approaches for retention. The studies should be research based and rigorous. They can be pure research studies, curriculum and design or literature reviews but they must be at a cutting edge level and be subject to detailed review and assessment.

As we navigate post-pandemic educational recovery and future-oriented design, the Handbook of Research in Online Learning: Insights and Advances emerges as a scholarly authority to illuminate existing questions and catalyze conversations on imperative transformations in education. Tailored for researchers, designers, educators, administrators, and stakeholders, this handbook delves into the nuanced landscape of online learning. Curated by leading experts, each chapter provides a deep exploration of critical online teaching and learning dimensions. Whether you're navigating the complexities of instructional design, exploring the impact of digital learning on diverse student populations, or delving into the transformative potential of AI, each chapter illuminates critical aspects of online education. It merges current significant works with unpublished manuscripts, embodying the interdisciplinary essence of online learning research. Grounded in diverse theoretical frameworks and research methods, it offers theoretical insights and actionable guidance for cutting-edge educational methodologies. This handbook is not just a compendium; it's an indispensable guide for shaping the future of education. Contributors are: Michael Ahlf, Stephen Allen, Tonya Amankwatia, Fatih Ari, Ismahan Arslan-Ari, Michael K. Barbour, Gail Alleyne Bayne, Karen Bellnier, M. Aaron Bond, Victoria Brown, George Bradford, William Cain, Sumie Chan, Lauren Cifuentes, Laura DaVinci, Gina Deckard, Shernette Dunn, Anne Fensie, Holly Fiock, Sara Flowers, Carla Karen Fortune, Theodore Frick, Michael M. Grant, Alexis Guethler, Dan He, Atsusi "2c" Hirumi, Charles B. Hodges, Stephanie Hostetter, Michael Houdyshell, Fethi A. Inan, Frank Jamison, Amir Kalan, Meryl Krieger, Jessica Lantz, Mary Lefaiver, Juhong Christie Liu, Noble Lo, Barbara Lockee, Fatemeh Marzban, Trey Martindale, Sara McNeil, Laura McNeill, Stephanie Moore, Martha Lorena Obermeier, Larisa Olesova, Jennifer Jihae Park, Sanghoon Park, Yujin Park, AnthTony Pina, Drew Polly, Yingxiao Qian, Thomas Reeves, Christiane Reilly, Jennifer Richardson, Aubrey Rogowski, Leanne Rutherford, Kay Seo, Sanga Song, Edwin Teye Sosi, Stefan Stenbom, Sharon Stidham, David Tai, Hengtao Tang, Torrey Trust, Shannon Tucker, Denis Unal, Lucas Vasconcelos, Charles Xiaoxue Wang, Florence Williams, Ying Xie and Fan Xu.

Leveraging the research efforts of more than sixty experts in the area, this book reviews cutting-edge practices in machine learning for financial markets. Instead of seeing machine learning as a new field, the authors explore the connection between knowledge developed by quantitative finance over the past forty years and techniques generated by the current revolution driven by data sciences and artificial intelligence. The text is structured around three main areas: 'Interactions with investors and asset owners,' which covers robo-advisors and price formation; 'Risk intermediation,' which discusses derivative hedging, portfolio construction, and machine learning for dynamic optimization; and 'Connections with the real economy,' which explores nowcasting, alternative data, and ethics of algorithms. Accessible to a wide audience, this invaluable resource will allow practitioners to include machine learning driven techniques in their day-to-day quantitative practices, while students will build intuition and come to appreciate the technical tools and motivation for the theory.

This volume contains selected expository lectures delivered at the 2018 Maurice Auslander Distinguished Lectures and International Conference, held April 25–30, 2018, at the Woods Hole Oceanographic Institute, Woods Hole, MA. Reflecting recent developments in modern representation theory of algebras, the selected topics include an introduction to a new class of quiver algebras on surfaces, called “geodesic ghor algebras”, a detailed presentation of Feynman categories from a representation-theoretic viewpoint, connections between representations of quivers and the structure theory of Coxeter groups, powerful new applications of approximable triangulated categories, new results on the heart of a t t-structure, and an introduction to methods of constructive category theory.

Teaching Gradually is a guide for anyone new to teaching and learning in higher education. Written for graduate student instructors, by graduate students with substantive teaching experience, this resource is among the first of its kind to speak to graduate students as comrades-in-arms with voices from alongside them in the trenches, rather than from far behind the lines. Each author featured in this book was a graduate student at the time they wrote their contribution. Consequently, the following chapters give scope to a newer, diverse generation of educators who are closer in experience and professional age to the book’s intended audience. The tools, methods, and ideas discussed here are ones that the authors have found most useful in teaching today’s students. Each chapter offers a variety of strategies for successful classroom practices that are often not explicitly covered in graduate training.Overall, this book consists of 42 chapters written by 51 authors who speak from a vast array of backgrounds and viewpoints, and who represent a broad spectrum of experience spanning small, large, public, and private institutions of higher education. Each chapter offers targeted advice that speaks to the learning curve inherent to early-career teaching, while presenting tangible strategies that readers can leverage to address the dynamic professional landscape they inhabit. The contributors’ stories and reflections provide the context to build the reader’s confidence in trying new approaches in their his or her teaching. This book covers a wide range of topics designed to appeal to graduate student instructors across disciplines, from those teaching discussion sections, to those managing studio classes and lab sessions, to those serving as the instructor of record for their own course. Despite the medley of content, two common threads run throughout this volume: a strong focus on diversity and inclusion, and an acknowledgment of the increasing shift to online teaching.As a result of engaging with Teaching Gradually, readers will be able to:·Identify best teaching practices to enhance student learning ·Develop a plan to implement these strategies in their teaching ·Expand their conception of contexts in which teaching and learning can take place ·Evaluate and refine their approaches to fostering inclusion in and out of the classroom ·Assess student learning and the efficacy of their own teaching practices ·Practice professional self-reflection