Oldal 92 [92]
OCR
9.5 CONCLUSIONS 91 Effective AVs can be powerful supplementary, complementary, and alternative tools to written presentations or verbal descriptions (Shaffer et al., 2007). On the other hand, as we have discussed above, AVs might have their own specific weak points (compared to written/verbal descriptions): too much visualized information can be harmful. By applying sequenced multiple AVs, the AlgoRythmics environment generates a three-phase learning experience: (1) watching the dance choreography illustration, (2) watching the abstract computer animation, (3) participating in the white-/black-box algorithm orchestration. Phase 3 harmonizes with the constructivist approach to learning: learners become active participants of the AV experience. From the perspective of Epistemic Fidelity theory: — During the first two phases, we tried to increase epistemic fidelity by common methods: « Adding graphical elements to the video recordings. ¢ Dancers/elements that had reached their final positions “turned back”/“were recoloured”. ¢ A pair of arrows directs user’s attention to the elements the current operation is applied to. « Expressive animations for comparison/swapping operations (The way we animate comparison operation transmits the idea of weighing the number pair to be compared by a balance). — During black-box tasks, we proposed to increase epistemic fidelity in a new way: by applying invisibility. While more research is needed to draw general conclusions in the studied topic (it is a limitation of this study that we investigated the proposed research questions only with respect to one specific sorting algorithm), the study we have performed reveals latent deficiencies that AV systems might have. Visualizing information that has extra meanings for human viewers can obstruct them in following strict computer algorithms. Research results show that wisely applied hiding may result in more effective AV due to its higher epistemic fidelity. As a final conclusion: Effective AV systems support not blind learners in assimilating the algorithm processing role of blind computers.
