# Generic Tasks for Algorithms
## 3 Results and Discussion  
The aggregated rating of the experts regarding the meaningfulness of the proposed GTs over all three input algorithms (\(m=3\cdot 14=42\)) is shown in Figure 3. Overall, the tasks were received rather positively. For GT 1, GT 2, and GT 3, the vast majority of the experts (73.81%) rather or completely agreed with the hypothesis that the corresponding tasks offer learning opportunities for the students. Noticeably, for the implementation task, almost half of the answers (47.62%) were \"Completely agree\". GT 1, the implementation task, is thus the most meaningful task to pose, according to the experts. The experts' opinions on the two Parsons problems, GT 4a and GT 4b, are rather similar. In comparison to the other GTs, they consider the Parsons problems less meaningful, as they received more neutral evaluations. This result is rather surprising, as empirical results, e.g., [24; 28], suggest that working on Parsons problems can be just as effective for students as when working on tasks similar to GT 1 and GT 3, i.e., writing code fulfilling a specified purpose or finding an error in a given algorithm. A possible explanation could be that, although the concept of a Parsons problem may be known to the experts, the corresponding results regarding its effectiveness may be not. Performing a calculation of the pair-wise correlations between the three different input algorithms for the raters, estimations regarding the six core CT skills showed that the results all correlate with each other (\(r_{12}=0.69,r_{13}=0.65,r_{23}=0.63\)). Thus, we decided to analyze the experts' evaluation of the six core CT skills regarding the proposed GTs independently from the input algorithms. We performed a statistical analysis over all \(m=3\cdot 14=42\) answers that were given for each skill and each of the specific tasks belonging to one GT, the results of which can be seen in Figure 4. Several observations can be made. The experts' judgment that GT 1, implementation, is the most meaningful one of the GTs fits together with their assessment that it addresses four of the six core CT skills, namely abstraction (with an arithmetic mean of \(M=3.02\)), algorithmic thinking (\(M=4.21\)), automation (\(M=3.24\)), and generalization (\(M=2.19\)), most strongly compared to the other four GTs. This supports the claim that writing code is the most challenging task for students compared to other programming activities, as stated in [21]. Additionally, for four out of the six CT skills, the results of the experts' ratings for GT 1 are the highest over all five GTs. Not surprisingly, as all tasks revolve around the working and learning processes with algorithms, the experts consider the skill of algorithmic thinking to be addressed most strongly by all GTs. Another result that was to be expected is that GT 3, find the error, addresses the skill of debugging the most, with an arithmetic mean of \(M=3.55\). Remarkably, we can report the highest variation of the experts' judgment for the skill of debugging for all five GTs. A possible explanation for this could be that the concept of debugging is mainly known as the activity of finding an error inside a program, rather than it being a CT skill for making assumptions about possible outcomes of algorithms and methods or testing one's own ideas for robustness [8]. As such, the concept of debugging in the context of CT is more general than tracing [23] in CS, which is described as the execution of a program line by line, and could rather be associated with the notion of debugging by some. Figure 3: Agreement with the statement “Students can learn a lot when completing this task” for \(n=14\) raters aggregated for the three different algorithms, resulting in \(m=42\) answers per GT. We can also observe that the variation of the ratings for most of the skills is relatively high. There are several possible explanations for this.