The present case study describes the results of the educational data mining aimed at predicting the undergraduate courses of computer science (BCA and B.Tech.) students’ instant dropout or after first semester. For this purpose, the preliminary data of 220 students collected randomly in prescheduled format on personal interview to find out dropout rates and dropout reasons. The simple and intui...

While MOOCs offer educational data at a new scale, many educators have been alarmed by their high dropout rates. Learners join a course with some motivation to persist for some or all of the course, but various factors, such as attrition or lack of satisfaction, can lead them to disengage or totally drop out. Educational interventions targeting such risk factors can help reduce dropout rates. H...

High school dropout is an important policy issue and its determinants are a longstanding interest of economics. However, very little is known on the roles of noncognitive traits in influencing school dropout decisions. We employ voluntary forgone health care as a proxy for the underlying noncognitive traits that may induce adolescents to dropout and estimate its effects on early school attritio...

Single-cell RNA sequencing (scRNA-seq) is a fast growing approach to measure the genome-wide transcriptome of many individual cells in parallel, but results in noisy data with many dropout events. Existing methods to learn molecular signatures from bulk transcriptomic data may therefore not be adapted to scRNA-seq data, in order to automatically classify individual cells into predefined classes...

Dropouts are common in longitudinal study. If the dropout probability depends on the missing observations at or after dropout, this type of dropout is called informative (or nonignorable) dropout (ID). Failure to accommodate such dropout mechanism into the model will bias the parameter estimates. We propose a conditional autoregressive model for longitudinal binary data with an ID model such th...

INTRODUCTION Understanding participant demographic characteristics that inform the optimal design of obesity RCTs have been examined in few studies. The objective of this study was to investigate the association of individual participant characteristics and dropout rates (DORs) in obesity randomized controlled trials (RCT) by pooling data from several publicly available datasets for analyses. W...

Within the pattern-mixture modeling framework for informative dropout, conditional linear models (CLMs) are a useful approach to deal with dropout that can occur at any point in continuous time (not just at observation times). However, in contrast with selection models, inferences about marginal covariate effects in CLMs are not readily available if nonidentity links are used in the mean struct...

A very commonly faced issue while training prediction models using machine learning is overfitting. Dropout is a recently developed technique designed to counter this issue in deep neural networks and has also been extended to other algorithms like SVMs. In this project, we formulate and study the application of Dropout to Hidden Unit Conditional Random Fields (HUCRFs). HUCRFs use binary stocha...

Previous reviews of premature termination have yet to examine whether disparate psychotherapy treatments differ in their dropout rates for specific disorders. Using data from 587 studies, a series of meta-analyses were conducted comparing dropout rates between treatment approaches for 12 separate disorder categories. Although, significant differences between treatment approaches were found for ...

INTRODUCTION The dropout level from the Danish medical schools is high, but we have only little insight into this problem. The purpose of this study was to qualify the ongoing discussions concerning dropout. MATERIAL AND METHODS In this retrospective cohort study, relevant variables were extracted from the established database of Aarhus University for the 639 students initiating medicine stud...