# Toward Adaptability of E-Evaluation: Transformation from Tree-Based to Graph-Based Structure
## 3 Proposed Competence Tree Improvements for Increase of Tasks Dataset Flexibility  
One idea of competence tree-based e-evaluation is a student's ability to select the learning path by themselves [17]. However, this approach has its limitations as, in some situations, the learning path flexibility is not acceptable or is in conflict with the student's profile. Sometimes learning paths should be generated to minimize a student's choices. While learning path generation strategies vary depending on a given situation, two main student-oriented strategies for knowledge evaluation paths exist: (i) incremental knowledge evaluation [20] and (ii) shortest path evaluation [21].  
Incremental knowledge evaluation requires taking into account task complexity and cognitive load. The evaluation path should start from simpler tasks and grow step by  
Figure 2: Example of a competence tree structure.  
step to more complex tasks. Therefore, this method is more suitable for learning process personalized implementation when the base flaws of students' knowledge are identified, and material is provided to fill the gap.  
Shortest path evaluation is more appropriate for knowledge evaluation when a student should demonstrate which level of knowledge they managed to achieve. Students with sufficient knowledge prefer to do as few tasks as possible rather than repetitively demonstrate the same skills and knowledge in different tasks. Therefore, evaluation paths should start with the most complex task. If the student is not able to solve the task, the complexity could be reduced step by step to obtain the achieved knowledge level of the student.  
Both of these knowledge evaluation paths take into account task complexity. However, one starts from the lowest level of complexity and increases if a student manages to solve it, while the other one starts from the highest complexity tasks and reduces the complexity if a student fails to solve it.  
Contextual graphs do not directly take into account task complexity, and a learning path strategy is constructed by the test designer. Competence trees present the relative complexity of tasks in different levels of the competence tree. However, there are no data on task complexity comparisons between tasks at the same level. Therefore, we add few small adjustments to the design and storage of the competence tree:  
1. All sub-competencies should be arranged based on their relative complexity. Therefore, in each branch of the competence tree, lower complexity tasks will be presented on the left and complexity will increase going to the right. This should be performed by the competence tree designer and might be based on personal opinion or historical data, which can be used to compare the complexity of competencies as well as tasks.
2. Competence should define whether a child's competencies are sequentially dependent or independent of each other. This is required to understand whether it is worth giving a sibling task of higher complexity if the student failed the lower complexity task.  
The modifications are minor; however, they are necessary for higher flexibility, adaptability, and transformation to graph-based e-evaluation solutions. The modifications require additional work from the competence tree designer. All sibling competencies should have their relative difficulty, and, in some cases, it might be difficult to identify them. However, spending time on the implementation of those modifications might generate new features for e-evaluation resource automated transformation and reusability.