OCR
10.2 PROMOTING ALGORITHMIC/COMPUTATIONAL THINKING 97 experiences (Jonassen, Peck, g Wilson, 1999). According to Wang (2009), such a learning environment assumes attentive and thoughtful design. We purposefully focused on implementing the following previous research results regarding effective CT promoter learning environments. — Why visually illustrated algorithms? Since computer algorithms are inherently abstract dynamic processes, AV has become the common approach to make them more tangible. We chose to visually illustrate how algorithms work by videotaped dance performances and computer-based animations (Shaffer et al., 2010). The meta-analysis performed by Höffler and Leutner (2007) emphasizes the educational superiority of representational animations compared to static pictures, especially when procedural-motor knowledge has to be assimilated. — Why sequenced multiple representation? The basic idea of using sequenced multiple AVs is that users can benefit from the properties of each representation (Meij & Jong, 2006). Two key attributes of multiple representations are complementarity and redundancy. Redundancy is essential to make learners able to relate to different representations. Complementary attributes can be used to implement the principle of progression with respect to the informational content, complexity, level of abstractness, and the control the learner has in the algorithm animation process. We use four different representations of the number sequence to be sorted: embodied by a dancer sequence, stored in a white-box array, stored in a black-box array, and illustrated as a colour scale bar. — Why interactive learning environment? The meta-study (Hundhausen et al., 2002) stresses the decisive role interactivity has in effective AVs. This study concludes that AVs promote effective learning when users are engaged actively in the visualization process (instead of passively viewing it). In other contexts, the same phenomenon was observed (e.g. Mork, 2011). To implement this principle of “genuine active involvement”, the software we designed invites users to orchestrate the studied algorithms (Mayer & Chandler, 2001). — Why applying selective hiding? As detailed in Chapter 9, applying hiding may support human viewers in assimilating the algorithm processing role of blind computers due to their higher epistemic fidelity (Kátai, 2014a). During the black-box-based algorithm orchestration processes, since the stored numbers are invisible, users are forced (as computers too) to perform the comparison operations explicitly (not only implicitly, in their minds) in order to realize whether the corresponding elements need or need not to be swapped. — Why pattern-recognition-oriented strategy? Since algorithms are in fact generalized patterns intended to solve problems, CT assumes pattern recognition and generalization skills (Wu & Richards, 2011). Humans are