# Data-Driven Personalized Learning Path Planning Based on Cognitive Diagnostic Assessments in MOOCs
## 4 Experiments and Results
### Personalized Learning Path Generation  
Experiments of the dynamic learning path planning algorithm were based on the Application Programming Interfaces (APIs) provided by Tsinghua University, the XuetangX MOOC platform and the MOOCCubeX dataset. Students followed a fundamental logical sequence of knowledge (i.e., the original path sequence) at the beginning of the study, as mastery of advanced knowledge depends on mastering prerequisite knowledge. Consequently, the personalized learning path planning approach proposed in this study used a feedback strategy to form a personalized learning path based on learning states.  
The personalized learning path planning algorithm is based on the knowledge difficulty model and knowledge mastery model mentioned in Sections 3.2 and 3.3, respectively. The learning path planning procedure is depicted as follows:  
1. Students learn in the original learning sequence.
2. When students are judged as having completed each chapter, their knowledge mastery status is automatically updated based on the knowledge mastery model. The algorithm will automatically assign the corresponding knowledge point for the student if the algorithm determines that the student has not fully mastered the knowledge.
3. Unlearned and unmastered knowledge is added to the review list. Moreover, the knowledge points that are insufficiently mastered for the prerequisite knowledge (unmastered) are also added to the review list.
4. Knowledge points at different levels are arranged based on the prerequisite relationship, and knowledge points at the same level are arranged from easy to difficult ranks.
5. After reviewing the list of knowledge points in the above order, the student completes the test again, and their state will be updated. If the student's state is still in unlearned (state = 1), unmastered (state = 2) or insufficiently mastered (state = 3) states in this chapter, then the process goes back to Step 3.) If the student fully grasps the knowledge (state = 4), then the process goes back to Step 1 and continues to the next suggested chapter.  
Figure 3 illustrates three typical learning paths generated by the proposed method. The learning paths shown in Figure 3a,b significantly differ from the paths planned by the algorithm, but the learning paths shown in Figure 3c coincide with the algorithm's planned paths.  
According to Figure 3a, the first typical learning path is in linear style. Students learn all knowledge points sequentially and without retrospection, resulting in a significant number of insufficiently mastered, unlearned and unmastered knowledge points in the learning progress that have not been handled. Therefore, this type of learning progress is ineffective. According to Figure 3b, the second typical learning path is circular. Throughout the learning process, students independently reviewed the knowledge points. Knowledge points that were insufficiently mastered were missed, while some mastered knowledge was repeatedly reviewed. Therefore, this type of learning progress is inefficient as well.  
Compared with Figure 3a,b, the third typical learning path automatically generated by the algorithm in Figure 3c precisely covers all unlearned, unmastered and insufficiently mastered knowledge. The knowledge points are also ranked based on prerequisite relationships and difficulty levels. This shows that the learning paths generated by the dynamic learning path planning algorithm are adaptive for students. The personalized learning path based on learning states saves time and increases learning efficiency.