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Abstract
The student learning performance is analyzed that we adopted the proposed Jacobian Matrix-based Learning Machine (JMLM). It is significant for establishing prediction machine learning model for student learning performance and these tool can help teacher to analyze the student data not difficult to analyze. Correct rate of our model is 87% and 86% better than traditional machine learning models.
Original language | English |
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Title of host publication | Emerging Technologies for Education - 2nd International Symposium, SETE 2017, Held in Conjunction with ICWL 2017, Revised Selected Papers |
Editors | Tien-Chi Huang, Rynson Lau, Yueh-Min Huang, Marc Spaniol, Chun-Hung Yuen |
Publisher | Springer Verlag |
Pages | 469-474 |
Number of pages | 6 |
ISBN (Print) | 9783319710839 |
DOIs | |
State | Published - 2017 |
Event | 2nd International Symposium on Emerging Technologies for Education, SETE 2017, held in Conjunction with the 16th International Conference on Web-based learning, ICWL 2017 - Cape Town, South Africa Duration: 20 Sep 2017 → 22 Sep 2017 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 10676 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 2nd International Symposium on Emerging Technologies for Education, SETE 2017, held in Conjunction with the 16th International Conference on Web-based learning, ICWL 2017 |
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Country/Territory | South Africa |
City | Cape Town |
Period | 20/09/17 → 22/09/17 |
Keywords
- Machine learning
- Parameter based methods
- Structure based methods
- Student performance
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