Programming Course Student Performance Prediction based on Feature Construction
Created by W.Langdon from
gp-bibliography.bib Revision:1.7047
- @InProceedings{Li:2022:DSIT,
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author = "Xiaobin Li and Chunli Xie and Shuqin Wang and
Xiehua Zhang",
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booktitle = "2022 5th International Conference on Data Science and
Information Technology (DSIT)",
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title = "Programming Course Student Performance Prediction
based on Feature Construction",
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year = "2022",
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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)",
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keywords = "genetic algorithms, genetic programming, Source
coding, Education, Decision making, Data science,
Information technology, Student Performance Prediction,
Feature Construction",
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DOI = "
doi:10.1109/DSIT55514.2022.9943955",
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month = jul,
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notes = "Also known as \cite{9943955}",
- }
Genetic Programming entries for
Xiaobin Li
Chunli Xie
Shuqin Wang
Xiehua Zhang
Citations