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Journal : Proceeding of the Electrical Engineering Computer Science and Informatics

Case Based Reasoning Adaptive E-Learning System Based On Visual-Auditory-Kinesthetic Learning Styles Abdul Rahman; Utomo Budiyanto
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1955

Abstract

Current technological developments have reached all fields including education. With the support of technology, teaching and learning activities can increase to a better level. The problem that occurs at this time in improving the quality of education is the difficulty of students to get grades that are in accordance with the Minimum Completeness Criteria, the difficulty of the teacher providing material in accordance with each student's learning style. This study aims to develop adaptive E-Learning to assist teachers in recommending material that is suitable for each student's learning style. This adaptive e-learning adopts a Visual Auditory Kinesthetic (VAK) learning style and to recommend material using the Case Based Reasoning (CBR) method. Student test results after using adaptive E-learning have fulfilled the Teaching Mastery Criteria with an average grade of 85. This suggests that under adaptive E-learning has been able to improve student grades.
Prediction Of Students Academic Success Using Case Based Reasoning Abdul Rahman; Rezza Anugrah Mutiarawan; Agung Darmawan; Yan Rianto; Mohammad Syafrullah
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1956

Abstract

Academic success for a student is influenced by many factors during their study period. Factors such as student gender, student absenteeism, parental satisfaction with schools, relations and parents who are responsible for students can influence student success in the academic field. Researchers try to find out what are the most dominant factors in determining academic success for a student at different levels of education such as elementary, middle and high school level. Previous research grouped the level of student academic success into three levels, namely low, medium, high and obtained 15 Association Rules Generated By Apriori Algorithm. This study tried to find out and predict the possible level of academic success of students by using 9 Association Rules Generated By Apriori Algorithm from previous research. The method used to predict the level of student academic success is case based reasoning with the nearest neighbor algorithm. By using the Association Rules Generated By Image Algorithm and with the data set from the xAPIEducational Mining Dataset the case similarity value was obtained with knowledge data that is 1 with a percentage of 81%, and data that had a similarity value of less than 1 was 19%. While in the previous study the best classification accuracy was 80.6% by the Voting classifier. And the grouping of success data is divided into two, namely low and high.