Studies of student risk of school dropout have shown that current predictors of “at-risk” status do not accurately identify a large percentage of students who eventually dropout. Through the analysis of the entire grade 1-12 longitudinal cohort-based grading histories of the class of 2006 for two school districts in the United States, this study extends past longitudinal conceptions of dropout ...

abstract Deep learning is the state-of-the-art in fields such as visual object recognition and speech recognition. This learning uses a large number of layers and a huge number of units and connections. Therefore, overfitting is a serious problem with it, and the dropout which is a kind of regularization tool is used. However, in online learning, the effect of dropout is not well known. This pa...

Dropout prediction in MOOCs is a well-researched problem where we classify which students are likely to persist or drop out of a course. Most research into creating models which can predict outcomes is based on student engagement data. Why these students might be dropping out has only been studied through retroactive exit surveys. This helps identify an important extension area to dropout predi...

Dropout and other feature noising schemes control overfitting by artificially corrupting the training data. For generalized linear models, dropout performs a form of adaptive regularization. Using this viewpoint, we show that the dropout regularizer is first-order equivalent to an L2 regularizer applied after scaling the features by an estimate of the inverse diagonal Fisher information matrix....

The main goal was to test if teacher-student relationships and achievement motivation are predicting dropout intention equally for low and high socio-economic status students. A questionnaire measuring teacher-student relationships and achievement motivation was administered to 2,360 French Canadian secondary students between 12 and 15 years old during the spring of 2005. A hierarchical multipl...

Dropout is a popular stochastic regularization technique for deep neural networks that works by randomly dropping (i.e. zeroing) units from the network during training. This randomization process allows to implicitly train an ensemble of exponentially many networks sharing the same parametrization, which should be averaged at test time to deliver the final prediction. A typical workaround for t...

Reports an error in "Meta-analysis of dropout in treatments for posttraumatic stress disorder" by Zac E. Imel, Kevin Laska, Matthew Jakupcak and Tracy L. Simpson (Journal of Consulting and Clinical Psychology, 2013[Jun], Vol 81[3], 394-404). There are two errors in the Results section. Each is described alongside the corrected results. Corrections did not influence interpretation of the results...

In this paper, a highly efficient and fast transient output capacitor-free low-dropout regulator (LDO) presented. The proposed LDO architecture is based on differential transconductance amplifiers pairing with push–pull stage to enable effective output driving capability. The slew rate at the gate of the output transistor ðSRGÞ is further enhanced by common mode-feedback (CMFB) resistors and a ...

AIM This paper aims to assess the dropout rate in different age groups through the example of the large cardiac rehabilitation centre affiliated with the Institute of Sports Medicine, University of Caxias do Sul. MATERIAL AND METHODS A historic cohort study comprising the following groups: Non-Old < 65 (n = 141); Young-Old 65-74 (n = 128); and Middle-Old 75-84 years old (n = 57). The exercise...