# A Personalized Learning Path Recommendation Method Incorporating Multi-Algorithm
## 4 Experiments and Results
### Result Analysis  
After many simulation experiments, part of the experimental results are extracted to analyse the usefulness and feasibility of the personalized learning path recommendation model investigated in this paper. In this paper, we use the apriori algorithm to generate the sequence of knowledge points, and in the apriori algorithm, the values of minsupport and minconfidence affect the accuracy of the algorithm, and the values of minsupport and minconfidence set too low will affect the accuracy of the algorithm, and set too high will cause the accuracy of the algorithm to decrease, therefore, in this paper, we design simulation experiments under different numbers of knowledge points to determine both according to the number of middle school mathematics knowledge points values. From Figure 2, the larger the values of minsupport and minconfidence, the higher the accuracy of the algorithm will increase. When minsupport varies between 0.4-0.6 and minconfidence varies between 0.8-1, the accuracy reaches the highest value, and then the accuracy tends to decrease when and continues to increase. Therefore, by experimental comparison, the values of minsupport and minconfidence are set to 0.53 and 0.91 in this paper. In this paper, the coverage of the generated knowledge point sequences is crucial for recommending personalized learning paths, since all knowledge points between learners' unmastered knowledge points and the target knowledge points are considered when generating the knowledge point sequences. In order to verify the coverage of the generated knowledge point sequences, this paper calculates the cognitive level of learners on the online learning platform based on the 584 knowledge points of junior high school mathematics, obtains the unmastered knowledge points of each learner, groups them into the  
Figure 2: Parameter setting. same set with the target knowledge points, and then generates knowledge point sequences based on the knowledge graph by the apriori algorithm and the correlation between the knowledge points, and calculates the learners' knowledge point. The experimental results from Table 4 show that the coverage of the knowledge point sequences generated for the learners by the method of this paper can reach more than 90%, which shows that the knowledge point sequences generated by the apriori algorithm and the correlation between knowledge points can more accurately generate the knowledge point sequences for the learners to meet their own needs. Based on the junior high school mathematics knowledge map, this paper uses the apriori algorithm to generate the sequence of knowledge points. To verify the feasibility of the model, this paper selects four different models to carry out comparative experiments with precision and coverage as indicators. These four models are the topological sorting algorithm (TO), firefly algorithm (FA), transfer learning method based on a knowledge map (TL-KG), and deep learning method (DL). During the experiment, the knowledge point data of 100 learners are selected and input into different models, and the output results of each model are recorded. In our simulation experiment results, the data of six randomly selected learners is shown in Table 5. According to Table 5, it can be seen that the apriori algorithm used in this paper outperforms the other four models in terms of precision and coverage, which indicates that the apriori algorithm is feasible and effective in generating sequences of knowledge points. The learners on the online learning platform were divided into an experimental group and a nonexperimental group, with 50 people in each group. Six learners were randomly selected from each group and analysed from the perspectives of academic performance and duration. Figure 3 shows that the learning duration of learners in the experimental group (recommended learning path) was less than that of learners in the nonexperimental group (not recommended learning path). In addition, the final learning performance is better than that of the nonexperimental group.