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This chapter focuses on computer science teaching methods that fit especially to be employed in the computer lab. The uniqueness of the computer lab as a learning environment for computer science is explained by the fact that it enables learners to explore their problem solving strategies, to express their solutions to a given problem, to get feedback regarding to the correctness of their solution and to reflect on it, to develop large projects, to explore new topics, and to deepen their understanding of the nature of the algorithms they develop. The aim of the lessons in the MTCS course that are dedicated to this topic is to expose the students to usages of the computer lab as a learning environment and to let them realize how it may improve their future pupils’ understanding of computer science ideas. One of the main messages of this chapter is that the learning of computer science in the computer lab is not limited to programming tasks; rather, the computer lab can be used in additional pedagogical ways that further enhance learners’ understanding of computer science. Specifically, the following topics are addressed in this chapter: what is a computer lab?, the lab-first teaching approach, visualization and animation, and using the Internet in the teaching of computer science.

This chapter* presents an active-learning-based teaching model for implementation in the MTCS course, which is based on the constructivist approach. This model is used in this Guide in most of the offered activities. The chapter starts with the motivation and the rationale for using active learning in the MTCS course; then, the active-learning-based teaching model is introduced and explained, including a description of the role of the instructor of the MTCS course in the model implementation.

This chapter proposes how to address computer science soft concepts and skills in the MTCS course. These topics are especially important today as they are dominant in the teaching of computer science in schools and the academia, as well as influence software development processes in the industry. These facts require from computer science students and teachers, computer scientists, and software engineers to acquire, in addition to disciplinary and technical knowledge, additional skills: both social (such as teamwork and getting and giving feedback), cognitive (e.g., thinking in terms of different levels of abstraction), and organizational (e.g., familiarity with different kinds of organizations). This chapter is divided into two parts: computer science soft concepts (Sect. 5.2) and computer science soft skills (Sect. 5.3). For each topic, its meaning and its importance and relevance in the context of computer science education are explained, and then, activities which deal with the said topic are presented.

This chapter focuses on teaching and learning of data science. We address this topic in this Guide of teaching computer science since data science is an emerging discipline that computer science is one of its basic components and, accordingly, the two fields have some overlaps. In this chapter we take into consideration the fact that not all the students, who study the MTCS course, are familiar with data science, and, therefore, unlike other chapters, this chapter does include some explanations about what data science is as well as its knowledge structure. We dedicate special attention to the pedagogy of data science, highlighting both the learners’ perspective and the teachers’ perspective. To avoid too abstract and vague description of the teaching and learning processes of data science in high school, we present an example of a data science program for high school. In the activities presented to the students in this chapter, we aim to highlight the opportunities that the teaching of data science opens to the students, as data science is an emerging field that its pedagogy is still in its initial development stages. At the same time, the activities highlight the relevance of data science to the teaching of computer science in order to deliver the message that the teaching processes of the two fields can mutually contribute to each other.