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...

Full description

Saved in:
Bibliographic Details
Published in:Neural computing & applications Vol. 34; no. 12; pp. 9369 - 9383
Main Authors: Su, Yingying, Wang, Shengxu, Li, Yi
Format: Journal Article
Language:English
Published: London Springer London 01.06.2022
Springer Nature B.V
Subjects:
Algorithms
Artificial Intelligence
Colleges & universities
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Data processing
Image Processing and Computer Vision
Machine learning
Neural networks
Performance prediction
Probability and Statistics in Computer Science
S.I.: AI-based Web Information Processing
Similarity
Special Issue on AI-based Web Information Processing
Student performance
Neural network
Machine learning
Prediction
ISSN:0941-0643, 1433-3058
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-021-06333-8