Prediction for Student Academic Performance Using SMNaive Bayes Model
- Springer Verlag
- Publication Type:
- Conference Proceeding
- Lecture Notes in Computer Science, 2019, 11888
- Issue Date:
|ADMA-19_Prediction for Student Academic Performance Using SMNaive Bayes Model.pdf||Published version||464.18 kB|
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
Predicting students academic performance is very important for students future development. There are a large number of students who can not graduate from colleges on time for various reasons every year. Nowadays, a large volume of students academic data has been generated in the process of promoting education informatization from the field of education. It becomes critical to predict student performance and ensure students to graduate on time by taking the best of these data. Machine learning models that predict students performance are widely available. However, some existing machine learning models still have the problem of low accuracy in predicting students performance. To solve this problem, we proposes a SMNaive Bayes (SMNB) model, which integrates Sequential Minimal Optimization (SMO) and Naive Bayes to make the prediction result more accurate. The basic idea is that the model predicts the performance of students professional courses via their basic course performance in the previous stage. In particular, SMO algorithm is leveraged to predict students academic performance of the first step and produces the results of the prediction; Naive Bayes then makes decision about the inconsistent results of the initial prediction; Lastly, the final results of students professional course performance prediction are produced. To test the effectiveness of our proposed model, we have conducted extensive experiments to compare SMNB against four prediction methods. The experimental results demonstrate that the proposed SMNB model is superior to all the compared methods.
Please use this identifier to cite or link to this item: