Programming Course Student Performance Prediction based on Feature Construction

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Li:2022:DSIT,

• author = "Xiaobin Li and Chunli Xie and Shuqin Wang and Xiehua Zhang",
• booktitle = "2022 5th International Conference on Data Science and Information Technology (DSIT)",
• title = "Programming Course Student Performance Prediction based on Feature Construction",
• year = "2022",
• abstract = "Programming course has become an important basic course for the cultivation of college students' computing thinking ability. Student performance prediction is very useful for assisting teachers' teaching, promoting students' learning and providing management decision-making suggestions. In the blended teaching environment, the students' homework and unit exam data collected by the online system are used to predict the students' performance at the end of the semester. This paper proposes to use the genetic programming method to construct the features of the collected data, generate more features that can be used for performance prediction, and finally predict the final exam. The experimental results show that the method used in this paper improves the accuracy of course performance prediction. (The datasets, source code and experimental result can be downloaded from https://gitee.com/wb0817002/spr)",
• keywords = "genetic algorithms, genetic programming, Source coding, Education, Decision making, Data science, Information technology, Student Performance Prediction, Feature Construction",
• DOI = "doi:10.1109/DSIT55514.2022.9943955",
• month = jul,
• notes = "Also known as \cite{9943955}",

}

Genetic Programming entries for Xiaobin Li Chunli Xie Shuqin Wang Xiehua Zhang

Citations