Teaching algorithms in upper secondary education is an essential topic because algorithms are fundamental concepts (Hromkovič & Lacher, 2017). In addition, algorithm development and understanding improves students’ problem solving and communication competences (Lessner, 2013). An algorithm can be described as a set of computational steps needed to transform an input into an output (Cormen, Leiserson, Rivest, & Stein, 2009). By using a programming language, an algorithm can be implemented in code to be executed by a computer. Programming languages `come and go' and are prone to developments and trends but algorithms are foundational (Schwill, 1994). Consequently, it is interesting to examine how to teach this topic and to discover teachers' practices and their underlying reasoning regarding teaching algorithms. To this end, Sentance and Csizmadia (2017) investigated teachers' perspectives on computing education in their search for effective pedagogies for teaching aspects of computer science e.g., algorithms. Results of their study showed several successful teaching approaches but also described challenges that teachers face, including the challenges of “students willingness or ability to problem solve” (Sentance & Csizmadia, 2017, p. 479). This highlights the need for sufficient education and adequate professional development to support teachers to respond to this challenge. Because computing education is expanding, many new teachers need to be educated (Leyzberg & Moretti, 2017; Yadav & Berges, 2019). To enrich teacher education on this topic, it is a widely used and effective way to examine the knowledge of experienced teachers and the broad range of variation within their knowledge (Verloop, Van Driel, & Meijer, 2001). Therefore, we are interested in the practices and knowledge of experienced teachers for teaching algorithms, specifically geared towards addressing students’ different abilities and various approaches to learning to solve problems. Teachers need a specific type of knowledge to teach algorithms effectively which is different from content knowledge or general pedagogy. Shulman (1987) described this knowledge as Pedagogical Content Knowledge (PCK), knowledge that “represents the blending of content and pedagogy into an understanding of how particular topics, problems, or issues are organized, represented, and adapted to the diverse interests and abilities of learners, and presented for instruction” (Shulman, 1987, p. 8). PCK concerns fundamental teacher knowledge, and, therefore, many researchers have examined the knowledge of, among others, science teachers (Alonzo & Kim, 2016; Henze, Van Driel, & Verloop, 2008). Prior PCK research into computer science education examined topics such as programming (Saeli, Perrenet, Jochems, & Zwaneveld, 2011), or design of digital artifacts (Rahimi, Barendsen, & Henze, 2016). However, there is little scientific understanding of teachers’ PCK for teaching algorithms. In this study, we focus on teachers’ PCK for teaching algorithms and we therefore interviewed seven computer science teachers.
|Publication status||Published - 8 Apr 2021|
|Event||2021 NARST Annual Conference - Online|
Duration: 7 Apr 2021 → 10 Apr 2021
|Conference||2021 NARST Annual Conference|
|Period||7/04/21 → 10/04/21|