Home > Archive > 2020 > Volume 10 Number 2 (Feb. 2020) >
IJMLC 2020 Vol.10(2): 346-351 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.2.941

Student Behavior Analysis Affecting Learning Achievement of Information Technology and Computer Science Students

Supaporn Bundasak

Abstract—The analysis of student behavior affecting the learning results with that data mining is research. To find behavioral factors that affects the student's academic performance. Learn more by the research team leading the reference information from collecting data from questionnaires of students of the Faculty of Science. To analyze the model of decision making to separate groups with similar behavior together and use grouping techniques (Clustering). Making decision to find out answers to behavioral factors that affect learning results. In small groups based on various behavioral factors such as Use of free time in everyday life Learning anxiety Concentrate on learning Motivation for learning and using K - Means Cluster and Random Tree to find a model that has Correctly Classified Instances when tested with 380 datasets of training data, with an average of 98% which is the average at a good level can Bring the model that has been Continued to develop the system.

Index Terms—Study behavior, learning results, data mining techniques, k-means, random tree.

The author is with Computer Science and Information Technology Department, Faculty of Science at Sriracha, Kasetsart University Sriracha Campus Chonburi, Thailand (e-mail: jumbundasak@hotmail.com, supaporn.band@ku.th).


Cite: Supaporn Bundasak, "Student Behavior Analysis Affecting Learning Achievement of Information Technology and Computer Science Students," International Journal of Machine Learning and Computing vol. 10, no. 2, pp. 346-351, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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