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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 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.
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