文章基本信息

标题：Predicting the Presence of Learning Motivation in Electronic Learning: A New Rules to Predict
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作者：Christina Juliane ; Arry A. Arman ; Husni S. Sastramihardja 等
期刊名称：TELKOMNIKA (Telecommunication Computing Electronics and Control)
印刷版ISSN：2302-9293
出版年度：2017
卷号：15
期号：3
页码：1223-1229
DOI：10.12928/telkomnika.v15i3.4286
语种：English
出版社：Universitas Ahmad Dahlan
其他摘要：Research affirms that electronic learning (e-learning) has a great deal of advantages to learning process, it’s provides learners with flexibility in terms of time, place, and pace. That easiness was not be a guarantee of a good learning outcomes though they got the same learning material and course, because the fact is the outcomes was not the same from one to another and even not satisfy enough. One of prerequisite to the successful of e-learning process is the presence of learning motivation and it was not easy to identify. We propose a novel model to predicting the presence of learning motivation in e-learning using those attributes that have been identified in previous research. This model has been built using WEKA toolkit by comparing fourteen algorithms for Tree Classifier and ten-fold cross validation testing methods to process 3.200 of data sets. The best accuracy reached at 91.1% and identified four parameters to predict the presence of learning motivation in e-learning, and aim to assist teachers in identify whether student needs motivation or does not. This study also confirmed that Tree Classifier still has the best accuracy to predict and classify academic performance, it was reached average 90.48% for fourteen algorithm for accuracy value.
关键词：predicting;learning motivation;tree classifier;WEKA;10-fold cross validation