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4.2 HOW CAN THE PRESENTED METHODS BE IMPROVED... 127 — generalizing and transferring the studied problem-solving process to a wide variety of problems (skill 6). The authors of a study (Mannila et al., 2014) stress that practising and developing CT-related skills is not limited to CS/STEM subjects. On the other hand, they argue that programming can be seen as a tool for developing all CT aspects if we clearly distinguish between the concepts of programming and coding. They suggest that coding should be seen as the last phase of the multifaceted programming process when the solution that has been achieved through such previous phases as analysis, decomposition, and design is implemented. Accordingly, while AlgoRythmics environment has the potential to promote CT for all students, it generates a learning experience that could be a prelude to the coding phase for those who are interested in it. 11.3.1 Shifting to blended learning The studies we presented in the previous chapters confirmed the potential of the AlgoRythmics environment to support users in assimilating the strategy of the analysed algorithms (skill 4). Our feedback from middle/high-school and university-level teachers also confirmed that they encourage self-paced learning mode mostly with the goal of discovering the strategy of the algorithm (YouTube comments support this aspect). This means — as we are going to show it in the following - that the potential of the learning environment to promote CT is only partially exploited. According to its ISTE/CSTA definition presented above, promoting CT implies more than understanding and performing algorithms, and we were taking this fact into account during the designing process. To improve these shortcomings, we suggest a shift to the principles of blended learning. Blended learning can be defined as an optimal mixture or the effective integration of various learning techniques, technologies, and delivery modalities to maximize the learning outcome (Singh & Reed, 2001; Valiathan, 2002; Finn & Bucceri, 2004). In line with this approach, we suggest in-person classroom activities facilitated and complemented by a teacher: while the students are studying the algorithms in the above described e-learning environment, the teacher guides their learning experiment to strengthen it (using techniques such as effective questioning). For example, the teacher can draw students’ attention to the way the principle of sequenced multiple representation was implemented: the number sequence is personified by a dancer sequence, stored in white and black arrays, and represented as colour bars (skill 3). Key attributes of multiple representations (complementarity and redundancy) should also be identified (Meij & Jong, 2006). In the following, we suggest other ways how teachers can provide CT support for students (during their journey with the AlgoRythmics learning tool).
