Editor/Reviewer loginFrequency : 12 issues per year
Subject : Computer Applications and Technology
ISSN : 2319–8656 (Online)
IJCATR Volume 5 Issue 8
Clustering Students By K-means
Mohammad Farzizadeh Ali Abdolahi
10.7753/IJCATR0508.1006
keywords : Clustering, Educational Data Mining
In typical assessment student are not given feedback, as it is harder to predict student knowledge if it is changing during testing. Intelligent Tutoring systems, that offer assistance while the student is participating, offer a clear benefit of assisting students, but how well can they assess students? What is the trade off in terms of assessment accuracy if we allow student to be assisted on an exam. In a prior study, we showed the assistance with assessments quality to be equal. In this work, we introduce a more sophisticated method by which we can ensemble together multiple models based upon clustering students. We show that in fact, the assessment quality as determined by the assistance data is a better estimator of student knowledge. The implications of this study suggest that by using computer tutors for assessment, we can save much instructional time that is currently used for just assessment.
@artical{m582016ijcatr05081006,
Title = "Clustering Students By K-means",
Journal ="International Journal of Computer Applications Technology and Research(IJCATR)",
Volume = "5",
Issue ="8",
Pages ="530 - 532",
Year = "2016",
Authors ="Mohammad Farzizadeh
Ali Abdolahi"}
Data mining, decision trees and neural network techniques have been investigated.
In the simulation, a performance evaluation of the algorithm is carried out.
In this research examined Clustering Students By K-means.
In this research the criteria used to assess the accuracy.