Bøger af Orit Hazzan

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.

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.

Demonstrating that computer science learning and teaching processes can be fun, thought-provoking and stimulating, this unique textbook presents both a conceptual framework and detailed implementation guidelines for computer science (CS) teaching.This highly-anticipated new edition has been updated with the latest teaching approaches and trends, and includes 110 learning activities (of which 15 are new). 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 of any specific institution, curriculum, programming language or paradigm.Topics and features: provides 110 detailed learning activities to be facilitated in different class settings; reviews curriculum and cross-curriculum topics in CS; explores the benefits of CS education research; describes strategies for cultivating problem-solving skills, for assessing learning processes, and for dealing with pupils' misunderstandings; proposes active-learning-based classroom teaching methods, including lab-based teaching; discusses various types of questions that a CS instructor or trainer can use for a range of teaching situations in class, homework and tests; investigates thoroughly issues of lesson planning and course design; examines the first field teaching experiences gained by CS teachers across different training frameworks.This preeminent textbook for CS teacher training programs draws on the authors' experience gained from three decades of teaching and training prospective and in-service CS teachers, as well as research in CS education. Concise, thorough and easy-to-follow, the book is also eminently suitable for use as a teaching guide for CS instructors at all levels.

This textbook presents both a conceptual framework and detailed implementation guidelines for computer science (CS) teaching.

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.

Overview and Goals The agile approach for software development has been applied more and more extensively since the mid nineties of the 20th century.