The jacobian matrix-based learning machine in student

Yi Zeng Hsieh, Mu Chun Su, Yu Lin Jeng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

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 languageEnglish
Title of host publicationEmerging Technologies for Education - 2nd International Symposium, SETE 2017, Held in Conjunction with ICWL 2017, Revised Selected Papers
EditorsTien-Chi Huang, Rynson Lau, Yueh-Min Huang, Marc Spaniol, Chun-Hung Yuen
PublisherSpringer Verlag
Pages469-474
Number of pages6
ISBN (Print)9783319710839
DOIs
StatePublished - 2017
Event2nd 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 201722 Sep 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10676 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Symposium on Emerging Technologies for Education, SETE 2017, held in Conjunction with the 16th International Conference on Web-based learning, ICWL 2017
Country/TerritorySouth Africa
CityCape Town
Period20/09/1722/09/17

Keywords

  • Machine learning
  • Parameter based methods
  • Structure based methods
  • Student performance

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