Orit Hazzan – författare

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 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.

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 book layouts historic and future perspectives at the introduction of technology into education systems: On the one hand, the book attempts to explain why despite numerous attempts, technology has struggled to integrate successfully into the education system for over a century; on the other hand, it explores whether this trend will persist in the foreseeable future, questioning if emerging technologies, like virtual reality or Gen-AI will ever be embraced by education systems worldwide, and introducing a hypothesis that these technologies will become inevitable so that education systems will have a little choice in adopting them. The underlying perspective is that education systems need to prepare for this new future and better start doing so now.

The book encompasses three key areas: education, technology, and future studies, with a focus on how technology will shape the future of education. It begins by examining past failures of integrating technology into education, analyzing the reasons behind these setbacks. It progresses to assess the potential integration of future technologies (10-20 years from now), exploring a feasible scenario and the force implications on learning, teachers, and the system.

Examining recent attempts to implement technology in education reveals numerous reasons for failure. A significant contributing factor appears to be inherent conflicts within the education system''s fundamental structure. These conflicts, involving goals, curricula, organizational structure, pedagogy, and student management, prevent the system from embracing reforms or new technologies.

Envisioning a future where technology will deeply ''know'' the students, ''sense'' their environment, ''understand'' the context and the situation, ''explain'' and ''advise'' them on the best suitable behavior or activity, the book anticipates applications in education ranging from ensuring personal safety and health to enhancing knowledge acquisition and decision-making. As the book explores the potential inevitability of technology in education, it recognizes the transformative impact on teachers and students and outlines possible desire scenario to aid in preparation, such as, personalized education to better suit student''s capabilities, needs, and desires; how to motivate students to learn in an environment where all tasks can be done by machines; ethical issues; the new role of the school, the educator, and the system, etc. This book is especially suitable for teachers, educators, public officials, and anyone interested in the future of education.

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 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.

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).