|Table of Contents|

Research on Undergraduate Academic Prediction Based on K-XGBoost Fusion Model(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2023年03期
Page:
89-97
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Research on Undergraduate Academic Prediction Based on K-XGBoost Fusion Model
Author(s):
Zhao Yuben1; Wang Xingning2; Li Chong1
(1.College of Engineering,Ocean University of China,Qingdao 266100,China)
(2.Teaching Center of Fundamental Courses,Ocean University of China,Qingdao 266100,China)
Keywords:
K-XGBoost;  academic performance prediction;  data mining;  machine learning;  ensemble learning
PACS:
TP181
DOI:
10.3969/j.issn.1001-4616.2023.03.012
Abstract:
High-precision prediction of academic conditions is an important technical means to improve the teaching level of colleges and promote teaching reform. At present,there are problems such as single data dimension and unbalanced data structure in academic prediction,which reduces the accuracy and generalization ability of the prediction model. To the end,this paper proposes a K-XGBoost academic situation prediction fusion model. Firstly,through accurate feature extraction and reconstruction,the model constructs a multi-dimensional set of academic features based on the database of the Academic Affairs Office of the University. Secondly,the clustering algorithm based on the minimum 2-norm is designed,and the unsupervised data balancing mechanism is innovatively established. Finally,the XGBoost integrated learning method based on loss function optimization designs the academic situation prediction module,and constructs a K-XGBoost learning situation prediction fusion algorithm with high accuracy and high generalization ability. The experimental results show that the predicted values of K-XGBoost models can well approximate the real values,and the MAE and RMSE of performance prediction results are reduced by 76.19% and 85.33% respectively compared with XGBoost models,which significantly improves the accuracy and generalization ability of the academic performance prediction.

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Last Update: 2023-09-15