In the education industry, needs of online learning are significantly increasing. However, web-based courses demonstrate higher dropout rates than traditional courses. As a result, engaging students with data analysis is getting more crucial especially for distance learning. this study, we analyze on daily status in order to predict student’s schools. Specifically, trained prediction machine le...

BACKGROUND Medical school attrition is important--securing a place in medical school is difficult and a high attrition rate can affect the academic reputation of a medical school and staff morale. More important, however, are the personal consequences of dropout for the student. The aims of our study were to examine factors associated with attrition over a ten-year period (2001-2011) and to stu...

Small-study effects are a common threat in systematic reviews and may indicate publication bias. Their existence is often verified by visual inspection of the funnel plot. Formal tests to assess the presence of funnel plot asymmetry typically estimate the association between the reported effect size and their standard error, the total sample size, or the inverse of the total sample size. In thi...

SUMMARY Selection models and pattern-mixture models are often used to deal with nonignorable dropout in longitudinal studies. These two classes of models are based on different factorizations of the joint distribution of the outcome process and the dropout process. We consider a new class of models, called mixed-effect hybrid models (MEHMs), where the joint distribution of the outcome process a...

Dropout from self-help cognitive-behavioral therapy for insomnia (CBT-I) potentially diminishes therapeutic effect and poses clinical concern. We analyzed the characteristics of subjects who did not complete a 6-week internet-based CBT-I program. Receiver operator characteristics (ROC) analysis was used to identify potential variables and cutoff for predicting dropout among 207 participants wit...

Recently, dropout has seen increasing use in deep learning. For deep convolutional neural networks, dropout is known to work well in fully-connected layers. However, its effect in convolutional and pooling layers is still not clear. This paper demonstrates that max-pooling dropout is equivalent to randomly picking activation based on a multinomial distribution at training time. In light of this...

Over the past 5 years, a great deal of attention has been paid to the development of early warning systems for dropout prevention. These warning systems use a set of indicators based on official school records to identify youth at risk for dropout and then appropriately target intervention. The current study builds on this work by assessing the extent to which a school disengagement warning ind...