# Toward Adaptability of E-Evaluation: Transformation from Tree-Based to Graph-Based Structure
## 2 Related Works  
### Existing Models for Adaptive E-Learning  
The development of e-learning environments has highlighted the importance of setting learners' initial levels of knowledge to provide course material appropriate to these levels of knowledge. [PERSON] and [PERSON] [1] created a skill-based e-learning environment in which all learners, after a skill test (which consists of five questions), are categorized into three levels (basic, normal, and advanced). Learning content is provided only based on skill evaluation reports. Unfortunately, this solution lacks dynamics. As such, [PERSON] et al. [2] developed a \"Learning' platform\" to introduce a personalization mechanism that automatically changes system complexity levels based on the flow of the student who interacts with the platform. Meanwhile, [PERSON] et al. [3] describe two approaches to maximize user grades for a course while respecting their time constraints. These approaches recommend successful paths based on available time and a user's initial knowledge level.  
[PERSON] et al. [7] present a hybrid model for detecting misconceptions using machine learning, as well as a technique for automatically modeling student learning and forgetting processes using a fuzzy inference system. The fuzzy inference system takes as input the level of knowledge of each student in one language and can diagnose his/her level of knowledge in another language. This allows the system to create an adaptive learning environment for each student. [PERSON] and [PERSON] [8] take not one input but propose an adaptive e-learning system based on different inputs for learning style and initial knowledge of learners, as measured by a preliminary evaluation across five sections. If the evaluation result meets or exceeds the standard grade set by the teacher, the student passes the section. If the result does not correspond to the standard grade, the student does not pass the section. These conditions affect the appearance of links in the menu area.  
Some researchers in e-learning systems have suggested adapting tests to learners' levels of knowledge. [PERSON] and [PERSON] [9] suggested an educational web-based e-evaluation system, which can be used by students for self-evaluation or by tutors for official examination. The test questions are marked as easy or advanced. This allows students to choose easy, advanced, or all questions in passing modes. [PERSON] et al. [10] expanded this approach and proposed a multilayer, intelligent, agent-based architecture for e-evaluation and e-learning systems. They increased test difficulty levels to three (easy, medium, and difficult) and divided them further into sub-levels to filter students who could take the difficult test. The easy level was divided into five sub-levels and the medium level was divided into three sub-levels. A learner first has to pass three easy level sub-tests and two sub-tests from medium level, and only then will they be able to take the advanced level tests.  
All these examples of adaptive e-learning and e-evaluation demonstrate a different logic that might be used for learning path construction, as well as different levels of adaptability which might be implemented, from material selection based on knowledge evaluation marks to an adaptive selection of further steps or tasks.