Research on the improvement effect of machine learning and neural network algorithms on the prediction of learning achievement
In order to improve the effect of college student performance prediction, based on machine learning and neural network algorithms, this paper improves the traditional data processing algorithms and proposes a similarity calculation method for courses. Moreover, this paper uses cosine similarity to c...
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| Vydáno v: | Neural computing & applications Ročník 34; číslo 12; s. 9369 - 9383 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
London
Springer London
01.06.2022
Springer Nature B.V |
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| ISSN: | 0941-0643, 1433-3058 |
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| Abstract | In order to improve the effect of college student performance prediction, based on machine learning and neural network algorithms, this paper improves the traditional data processing algorithms and proposes a similarity calculation method for courses. Moreover, this paper uses cosine similarity to calculate the similarity of courses. Simultaneously, this paper proposes an improved hybrid multi-weight improvement algorithm to improve the cold start problem that cannot be solved by traditional algorithms. In addition, this paper combines the neural network structure to construct a model framework structure, sets the functional modules according to actual needs, and analyzes and predicts students' personal performance through student portraits. Finally, this paper designs experiments to analyze the effectiveness of the model proposed in this paper. From the experimental data, it can be seen that the model proposed in this paper basically meets the expected requirements. |
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| AbstractList | In order to improve the effect of college student performance prediction, based on machine learning and neural network algorithms, this paper improves the traditional data processing algorithms and proposes a similarity calculation method for courses. Moreover, this paper uses cosine similarity to calculate the similarity of courses. Simultaneously, this paper proposes an improved hybrid multi-weight improvement algorithm to improve the cold start problem that cannot be solved by traditional algorithms. In addition, this paper combines the neural network structure to construct a model framework structure, sets the functional modules according to actual needs, and analyzes and predicts students' personal performance through student portraits. Finally, this paper designs experiments to analyze the effectiveness of the model proposed in this paper. From the experimental data, it can be seen that the model proposed in this paper basically meets the expected requirements. |
| Author | Li, Yi Su, Yingying Wang, Shengxu |
| Author_xml | – sequence: 1 givenname: Yingying surname: Su fullname: Su, Yingying email: 2005008@muc.edu.cn, syylesyu@163.com organization: School of Mechanical Engineering, Shenyang University – sequence: 2 givenname: Shengxu surname: Wang fullname: Wang, Shengxu organization: School of Mechanical Engineering, Shenyang University – sequence: 3 givenname: Yi surname: Li fullname: Li, Yi organization: School of Mechanical Engineering, Shenyang University |
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| Cites_doi | 10.3102/0002831214549453 10.1016/j.jsp.2016.06.001 10.1016/j.enconman.2013.10.060 10.1148/radiol.2016150409 10.1037/edu0000125 10.1016/j.eswa.2014.10.040 10.1016/j.cedpsych.2014.03.004 10.1016/j.energy.2015.08.019 10.1016/j.dsp.2014.05.008 10.1002/pits.21940 10.1111/1756-2171.12062 10.1038/s41467-018-07882-8 10.1016/j.labeco.2016.12.008 10.1016/j.intell.2018.05.006 10.1021/pr501173s 10.17105/SPR45-2.250-267 10.2298/PSI1604357M 10.1007/s10212-017-0361-x 10.1109/TIP.2017.2740564 10.1021/acs.chemmater.6b04663 10.1016/j.ssci.2015.08.008 10.1016/j.solener.2018.06.092 |
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| Keywords | Student performance Neural network Machine learning Prediction |
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| Title | Research on the improvement effect of machine learning and neural network algorithms on the prediction of learning achievement |
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