17,939 research outputs found
Culture and E-Learning: Automatic Detection of a Users’ Culture from Survey Data
Knowledge about the culture of a user is especially important for the design
of e-learning applications. In the experiment reported here, questionnaire
data was used to build machine learning models to automatically predict the
culture of a user. This work can be applied to automatic culture detection
and subsequently to the adaptation of user interfaces in e-learning
Teaching statistics in the physics curriculum: Unifying and clarifying role of subjective probability
Subjective probability is based on the intuitive idea that probability
quantifies the degree of belief that an event will occur. A probability theory
based on this idea represents the most general framework for handling
uncertainty. A brief introduction to subjective probability and Bayesian
inference is given, with comments on typical misconceptions which tend to
discredit it and comparisons to other approaches.Comment: 15 pages, LateX, 1 eps figure, corrected some typos. Invited paper
for the American Journal of Physics. This paper and related work are also
available at http://www-zeus.roma1.infn.it/~agostini
KOMPARASI METODE DECISION TREE, NAIVE BAYES DAN K-NEAREST NEIGHBOR PADA KLASIFIKASI KINERJA SISWA
In education, student performance is an important part. To achieve good and quality student performance requires analysis or evaluation offactors that influence student performance. The method still using an evaluation based only on the educator's assessment of information on theprogress of student learning. This method is not effective because information such as student learning progress is not enough to form indicators in evaluating student performance and helping students and educators to make improvements in learning and teaching. Previous studies have been conducted but it is not yet known which method is best in classifying student performance. In this study, the Decision Tree, Naive Bayes and K-Nearest Neighbor methods were compared using student performance datasets. By using the Decision Tree method, the accuracy is 78.85, using the Naive Bayes method, the accuracy is 77.69 and by using the K-Nearest Neighbor method, the accuracy is79.31. After comparison the results show, by using the K-Nearest Neighbor method, the highest accuracy is obtained. It concluded that the KNearest Neighbor method had better performance than the Decision Tree and Naive Bayes method
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