# Toward Adaptability of E-Evaluation: Transformation from Tree-Based to Graph-Based Structure
## 1 Introduction  
Much attention is currently being devoted to the personalization of e-learning systems [1, 2, 3]. However, there are some challenges in developing personalized learning systems. One of the challenges is the adaptation of learning material to fit learners' needs and the improvement of learning efficiency [1, 3]. This includes task identification and selection of the right difficulty level [4, 5]. Only tasks of the right complexity increase a learner's motivation and cause a state of flow. Flow is \"the state in which people are so intensely involved in an activity that nothing else seems to matter; the experience itself is so enjoyable that people will do it even at great cost, for the sheer sake of doing it\" [4]. One way to feel the flow is \"when a person's skills are fully involved in overcoming a challenge that is just about manageable, so it acts as a magnet for learning new skills and increasing challenges. If challenges are too low, one gets back to flow by increasing them. If challenges are too great, one can return to the flow state by learning new skills\" [5]. Therefore, learners need to be given tasks and learning material of the right complexity which are presented in the right order. To achieve this, an adaptive knowledge assessment system that would propose questions based on previous correct and/or incorrect answers in real-time is needed. However manual design and monitoring of students' learning paths are resource-consuming and time-inefficient tasks, and automation is preferable in this area [6]. In adaptive learning, the same learning material might be presented in adifferent form or different order to provide an individualized learning path. Therefore, it is important to store learning material in a form suitable for reuse and transformation into different forms.  
The need for resource reuse and usage variety is very noticeable in the knowledge evaluation area: the same question/task can be used in different courses or tests; the purpose of the testing might vary from knowledge evaluation to self-assessment, etc. Therefore, this paper aims to increase e-evaluation resource adaptability, mimicking the face-to-face evaluation feature to select the next question based on evaluative purposes and a given student's response to a current task. To achieve this goal, the following key challenges have to be resolved:  
1. Find the appropriate structure to store a tasks dataset for usage in adaptive learning.
2. Offer automatic test transformation/generation from the stored tasks dataset, dedicated for different purpose knowledge assessment tests.  
By solving these rising challenges, new ideas were proposed in this paper. One idea involved the improvement of the competence tree-based structure for a more accurate competence difficulty ranking. Another one involved two different transformations from a competence tree-based structure to a graph-based e-evaluation structure. Those transformations support any material reusability requirement and, at the same time, increase e-evaluation adaptability, as they incorporate different approaches for e-evaluation. All the mentioned ideas allow for a transition from human-based learning resource reusability to different forms and/or purpose knowledge evaluations resources.  
The structure of the paper is as follows. Related works in the field of adaptive learning and automated knowledge evaluation are reviewed in Section 2. Section 3 presents new ideas for the improvement of the competence tree to increase its adaptability for automated e-evaluation. Section 4 presents proposed methods dedicated to transforming the competence tree into different graph-based automated e-evaluation systems. The proposed storage structure and transformations are validated in Section 5 by comparing the results with contextual graphs and their transformations. The paper is summarized with the conclusion and future works section.