Article ; Online: Combination prediction method of students' performance based on ant colony algorithm.
2024 Volume 19, Issue 3, Page(s) e0300010
Abstract: Students' performance is an important factor for the evaluation of teaching quality in colleges. The prediction and analysis of students' performance can guide students' learning in time. Aiming at the low accuracy problem of single model in students' ... ...
| Abstract | Students' performance is an important factor for the evaluation of teaching quality in colleges. The prediction and analysis of students' performance can guide students' learning in time. Aiming at the low accuracy problem of single model in students' performance prediction, a combination prediction method is put forward based on ant colony algorithm. First, considering the characteristics of students' learning behavior and the characteristics of the models, decision tree (DT), support vector regression (SVR) and BP neural network (BP) are selected to establish three prediction models. Then, an ant colony algorithm (ACO) is proposed to calculate the weight of each model of the combination prediction model. The combination prediction method was compared with the single Machine learning (ML) models and other methods in terms of accuracy and running time. The combination prediction model with mean square error (MSE) of 0.0089 has higher performance than DT with MSE of 0.0326, SVR with MSE of 0.0229 and BP with MSE of 0.0148. To investigate the efficacy of the combination prediction model, other prediction models are used for a comparative study. The combination prediction model with MSE of 0.0089 has higher performance than GS-XGBoost with MSE of 0.0131, PSO-SVR with MSE of 0.0117 and IDA-SVR with MSE of 0.0092. Meanwhile, the running speed of the combination prediction model is also faster than the above three methods. |
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| MeSH term(s) | Humans ; Algorithms ; Neural Networks, Computer ; Machine Learning ; Students |
| Language | English |
| Publishing date | 2024-03-11 |
| Publishing country | United States |
| Document type | Journal Article |
| ZDB-ID | 2267670-3 |
| ISSN | 1932-6203 ; 1932-6203 |
| ISSN (online) | 1932-6203 |
| ISSN | 1932-6203 |
| DOI | 10.1371/journal.pone.0300010 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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