In context-based education, authentic situations ('contexts') are used as starting points for learning content matter ('concepts'). In this way, contexts provide significance and meaning to the concepts taught. The context-based approach has been investigated extensively in the field of science education.Context-based education has the potential to be a useful strategy in computer science (CS), in particular for teaching and learning of fundamental concepts. Initiatives like Informatik im Kontext confirm that context-based teaching and learning is a promising approach. So far, however, little research has been done on particular aspects of context-based learning in CS, such as the effective selection of contexts, principles for connecting concepts and contexts, and mechanisms for fostering knowledge transfer.This work-in-progress paper reports on an ongoing qualitative study on context-based teaching of fundamental CS concepts connected to algorithmic thinking. The study focuses on experiences and ideas of teachers, as they play a key role in the adaptation of contexts stemming from a rapidly changing field.We conducted semi-structured interviews with CS teachers on the above aspects of context-based teaching. The results reveal various ideas that teachers have on the use and effects of context-based learning and raises questions about the selection of contexts.

Background and Context: Computing education is expanding, while the teaching of algorithms is less well studied. Objective: The aim of this study was to examine teachers' pedagogical content knowledge (PCK) for teaching algorithms. Method: We conducted semi-structured interviews with seven computer science (CS) teachers in upper secondary education (students aged 15-18). The data were analyzed qualitatively. Findings: We found two patterns of variation in teachers' PCK. First, we detected variation in the teachers' goals related to their view of algorithms: they either focused on "thinking" about the algorithm as an object, or focused on "thinking and making", where the algorithm is also regarded as a program. Second, we found variation in teachers' knowledge about responding to differences between students, which may be generic or topic-specific. Furthermore, our findings reveal that teachers consider class discussions to play a significant role as an instructional method for provoking reflection. Implications: Our findings regarding PCK may be beneficial for the development of teacher education and professionalization activities for CS teachers.

Background and Context: Although context-based teaching and learning has been investigated extensively in science education, little is known regarding the use of contexts for teaching CS in secondary education. Objective: The aim of this study was to examine the characteristics of contexts suitable for teaching algorithms and to investigate teachers' considerations regarding those contexts. Method: This study examines teachers' practices and reasoning concerning the use of contexts and is based on explorative, empirical research. Data were collected through semi-structured interviews with seven CS teachers and analyzed qualitatively. Findings: The results of this study reveal several characteristics of effective contexts for teaching algorithms and show teachers' ambitions to address the variation within the student population when selecting contexts that advance students' algorithmic thinking. Implications: The found characteristics may serve as recommendation for designing contexts. Development of teacher education and professionalization activities may benefit from the discussion of teachers' motives and concerns.

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Teaching algorithmic thinking enables students to use their knowledge in various contexts to reuse existing solutions to algorithmic problems. The aim of this study is to examine how students recognize which algorithmic concepts can be used in a new situation. We developed a card sorting task and investigated the ways in which secondary school students arranged algorithmic problems (Bebras tasks) into groups using algorithm as a criterion. Furthermore, we examined the students' explanations for their groupings. The results of this qualitative study indicate that students may recognize underlying algorithmic concepts directly or by identifying similarities with a previously solved problem; however, the direct recognition was more successful. Our findings also include the factors that play a role in students' recognition of algorithmic concepts, such as the degree of similarity to problems discussed during lessons. Our study highlights the significance of teaching students how to recognize the structure of algorithmic problems.