This work describes the application of management theories in STEM (Science, Technology, Engineering and Mathematics) education systems. Two chapters examine STEM education on the K-12 national level and one chapter focuses on the higher education institutional level. All chapters are based on comprehensive research. Thus, it will appeal to teachers, school principals, researchers, graduate students, government policymakers, and all practitioners who care about STEM education in schools, academia and government. In each chapter, SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis is used as a managerial strategic tool for the examination of factors that focus either on internal circumstances (strengths and weaknesses), or external ones (opportunities and threats).

This work illustrates how risk management can be applied to educational systems in general, and STEM (Science, Technology, Engineering and Mathematics) education in particular. The rationale for this approach stems from the increased awareness of the importance and contribution of STEM education to nations' economic growth and development. The coverage begins with the challenges of STEM education systems, and concludes with a thorough strategic risk response plan. The text outlines a risk-management plan/program for STEM education in Israel, based on the conceptions of five stakeholders groups: educators, academics, industry professionals, military and philanthropic actors. All of whom have expressed interest in promoting STEM education in the high school/secondary education system. The result, ultimately, presents an impressive, meaningful, and practical understanding of the difficulties and challenges, together with applicable modes of action, and a new horizon towards which STEM Education should march.

This Brief presents a new model for business development-MERge-to be implemented in practitioners' professional development in general and in the context of STEM (Science, Technology, Engineering and Mathematics) initiatives, particularly, in industry, educational institutions and public sector organizations. The authors aim to contribute to the field of innovation and entrepreneurship by merging and consolidating different methodologies and insights borrowed from the "meta-professions" (referring to skills that can be expressed meaningfully after one has gained disciplinary and professional knowledge) of management, education, and research. Targeting three key groups-practitioners in industry, academic institutions and public sector organizations-this model proposes that all practitioners can further develop their unique expertise, as well as new skills, while acknowledging and applying the three meta-professions in their initiatives, on-going work and personal lives. The authors acknowledge that in the postmodern era, where barriers between disciplines are falling in every aspect of professional life, managerial, educational and research skills are becoming increasingly essential and interdependent. Featuring case studies that illustrate how the MERge model is implemented in practice, this volume presents practical tools for integrating these key skills in a wide variety of initiatives in business, teaching and research contexts.

The message conveyed in this work is that agility can be implemented anywhere. Accordingly, ten guidelines are presented for the adoption of agility to enable us to cope with changes in our lives, in our teams, and in our organizations. Since the authors advocate agility, the content is presented in the form of concise standalone chapters, allowing the reader to focus on the specific topic they wish to adopt in order to become agile.

This guide presents both a conceptual framework and detailed implementation guidelines for general computer science (CS) teaching. The content is clearly written and structured to be applicable to all levels of CS education and for any teaching organization, without limiting its focus to instruction for any specific curriculum, programming language or paradigm. Features: presents an overview of research in CS education; examines strategies for teaching problem-solving, evaluating pupils, and for dealing with pupils' misunderstandings; provides learning activities throughout the book; proposes active-learning-based classroom teaching methods, as well as methods specifically for lab-based teaching; discusses various types of questions that a CS instructor, tutor, or trainer can use for a range of different teaching situations; investigates thoroughly issues of lesson planning and course design; describes frameworks by which prospective CS teachers gain their first teaching experience.

This book contains the refereed proceedings of the 12th International Conference on Agile Software Development, XP 2011, held in Madrid, Spain, in May 2011.The year 2011 marked the 10th anniversary of the Agile Manifesto. In this spirit, the XP conference continued its fine tradition of promoting agility by disseminating new research results in a timely manner and by bringing together researchers and practitioners for a fruitful mutual exchange of experiences. As introduced for XP 2010, there were again two different program committees, one for research papers and one for experience reports. Regarding the research papers, 11 out of 56 submissions were accepted as full papers; and as far as the experience reports were concerned, the respective number was 4 out of 17 submissions. In addition to these papers, this volume also includes the short research papers, the abstracts of the posters, the position papers of the PhD symposium, and the abstracts of the workshops.

Data science is a new field that touches on almost every domain of our lives, and thus it is taught in a variety of environments. Accordingly, the book is suitable for teachers and lecturers in all educational frameworks: K-12, academia and industry. This book aims at closing a significant gap in the literature on the pedagogy of data science. While there are many articles and white papers dealing with the curriculum of data science (i.e., what to teach?), the pedagogical aspect of the field (i.e., how to teach?) is almost neglected. At the same time, the importance of the pedagogical aspects of data science increases as more and more programs are currently open to a variety of people. This book provides a variety of pedagogical discussions and specific teaching methods and frameworks, as well as includes exercises, and guidelines related to many data science concepts (e.g., data thinking and the data science workflow), main machine learning algorithms and concepts (e.g., KNN, SVM, Neural Networks, performance metrics, confusion matrix, and biases) and data science professional topics (e.g., ethics, skills and research approach). Professor Orit Hazzan is a faculty member at the Technion’s Department of Education in Science and Technology since October 2000. Her research focuses on computer science, software engineering and data science education. Within this framework, she studies the cognitive and social processes on the individual, the team and the organization levels, in all kinds of organizations. Dr. Koby Mike is a Ph.D. graduate from the Technion's Department of Education in Science and Technology under the supervision of Professor Orit Hazzan. He continued his post-doc research on data science education at the Bar-Ilan University, and obtained a B.Sc. and an M.Sc. in Electrical Engineering from Tel Aviv University.

This concise yet thorough textbook presents an active-learning model for the teaching of computer science. Offering both a conceptual framework and detailed implementation guidelines, the work is designed to support a Methods of Teaching Computer Science (MTCS) course, but may be applied to the teaching of any area of computer science at any level, from elementary school to university. This text is not limited to any specific curriculum or programming language, but instead suggests various options for lesson and syllabus organization. Fully updated and revised, the third edition features more than 40 new activities, bringing the total to more than 150, together with new chapters on computational thinking, data science, and soft concepts and soft skills. This edition also introduces new conceptual frameworks for teaching such as the MERge model, and new formats for the professional development of computer science educators. Topics and features: includes an extensive set of activities, to further support the pedagogical principles outlined in each chapter; discusses educational approaches to computational thinking, how to address soft concepts and skills in a MTCS course, and the pedagogy of data science (NEW); focuses on teaching methods, lab-based teaching, and research in computer science education, as well as on problem-solving strategies; examines how to recognize and address learners' misconceptions, and the different types of questions teachers can use to vary their teaching methods; provides coverage of assessment, teaching planning, and designing a MTCS course; reviews high school teacher preparation programs, and how prospective teachers can gain experience in teaching computer science. This easy-to-follow textbook and teaching guide will prove invaluable to computer science educators within all frameworks, including university instructors and high school teachers, as well as to instructors of computer science teacher preparation programs.