Designing Student Motivation Modeling System for Adaptive E-Learning using Naive Bayes Classifier

Enik Rukiati, Khafidurrohman Agustianto, Syamsiar Kautsar, Prawidya Destarianto


Computer  Aided Education (CAE) is one of information technology application in education.  CAE  was  first  introduced  in  the  early  1960s  at  Stanford  University  by  professor psychology Patrick Supper and Ricard C. Atkinson with their experiments used computers to teach  math  and  reading  to  elementary  school  students  in  East  Palo  Alto,  California.  The practice  of  applying  e-learning  in  universities  is  limited  to  accommodate  the  tasks  and materials  for  teachers,  whereas  with  the  paradigm  of  quality  education  and  Student  Centered Learning  (SCL),  E-Learning  is  required  to  support  both  teacher  and  student.  So,  E-Learning must also be able to understand students and present the material in accordance with the needs of  students.  This  study  aims  to  develop  Adaptive  E-Learning  uses  students’  motivation  as  a variable. Students’ motivation is modeled by using Naïve Bayes Classification, the result of the test  is  96.34  %  accurate.  The  results  of  this  modeling  are  then  used  as  variables  in  the  rule- bases  of  the  material  content  system  in  determining  the  adaptive  material  presentation  to  the needs of the students. Based on the accuracy of test results system developed in the research is expected  to  be  an  E-Learning  solution  that  supports  the  paradigm  of  quality  education  and Student Centered Learning (SCL).

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