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"SCHOOL DROPOUT"
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High school dropout, graduation, and completion rates : better data, better measures, better decisions
High school graduation and dropout rates have long been used as indicators of educational system productivity and effectiveness and of social and economic well being. While determining these rates may seem like a straightforward task, their calculation is in fact quite complicated. How does one count a student who leaves a regular high school but later completes a GED? How does one count a student who spends most of his/her high school years at one school and then transfers to another? If the student graduates, which school should receive credit? If the student drops out, which school should take responsibility? This book addresses these issues and to examine (1) the strengths, limitations, accuracy, and utility of the available dropout and completion measures; (2) the state of the art with respect to longitudinal data systems; and (3) ways that dropout and completion rates can be used to improve policy and practice.--Publisher's description.
Book
CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data
Most existing dimensionality reduction and clustering packages for single-cell RNA-seq (scRNA-seq) data deal with dropouts by heavy modeling and computational machinery. Here, we introduce CIDR (Clustering through Imputation and Dimensionality Reduction), an ultrafast algorithm that uses a novel yet very simple implicit imputation approach to alleviate the impact of dropouts in scRNA-seq data in a principled manner. Using a range of simulated and real data, we show that CIDR improves the standard principal component analysis and outperforms the state-of-the-art methods, namely t-SNE, ZIFA, and RaceID, in terms of clustering accuracy. CIDR typically completes within seconds when processing a data set of hundreds of cells and minutes for a data set of thousands of cells. CIDR can be downloaded at
https://github.com/VCCRI/CIDR
.
Journal Article
Examining the Impact of Policy and Practice Interventions on High School Dropout and School Completion Rates: A Systematic Review of the Literature
The purpose of this literature review is to systematically examine policy and practice intervention research and assess the impact of those interventions on high school dropout and school completion rates. This systematic review extends the literature by (a) describing both policy and practice interventions, (b) synthesizing findings from experimental or quasi-experimental research, and (c) examining the common elements of effective interventions. Specifically, this review addresses two main questions. First, what are the characteristics of the empirical literature examining high school dropout or school completion interventions? Second, what are the common elements of effective policy or practice interventions for reducing high school dropout rates or increasing school completion rates? Findings indicate that despite research highlighting the need to address multiple risk factors and the need for early intervention, the bulk of current empirical research is focused on single-component, individual, or small group interventions delivered at the high school level. Further research is needed to provide guidance to schools regarding the integration of dropout efforts with other school initiatives. Multitiered frameworks of support are suggested as a structure for accomplishing this effectively and efficiently.
Journal Article
ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis
Single-cell RNA-seq data allows insight into normal cellular function and various disease states through molecular characterization of gene expression on the single cell level. Dimensionality reduction of such high-dimensional data sets is essential for visualization and analysis, but single-cell RNA-seq data are challenging for classical dimensionality-reduction methods because of the prevalence of dropout events, which lead to zero-inflated data. Here, we develop a dimensionality-reduction method, (Z)ero (I)nflated (F)actor (A)nalysis (ZIFA), which explicitly models the dropout characteristics, and show that it improves modeling accuracy on simulated and biological data sets.
Journal Article
A general and flexible method for signal extraction from single-cell RNA-seq data
Single-cell RNA-sequencing (scRNA-seq) is a powerful high-throughput technique that enables researchers to measure genome-wide transcription levels at the resolution of single cells. Because of the low amount of RNA present in a single cell, some genes may fail to be detected even though they are expressed; these genes are usually referred to as dropouts. Here, we present a general and flexible zero-inflated negative binomial model (ZINB-WaVE), which leads to low-dimensional representations of the data that account for zero inflation (dropouts), over-dispersion, and the count nature of the data. We demonstrate, with simulated and real data, that the model and its associated estimation procedure are able to give a more stable and accurate low-dimensional representation of the data than principal component analysis (PCA) and zero-inflated factor analysis (ZIFA), without the need for a preliminary normalization step.
Single-cell RNA sequencing (scRNA-seq) data provides information on transcriptomic heterogeneity within cell populations. Here, Risso et al develop ZINB-WaVE for low-dimensional representations of scRNA-seq data that account for zero inflation, over-dispersion, and the count nature of the data.
Journal Article
Juvenile Arrest and Collateral Educational Damage in the Transition to Adulthood
Official sanctioning of students by the criminal justice system is a long-hypothesized source of educational disadvantage, but its explanatory status remains unresolved. Few studies of the educational consequences of a criminal record account for alternative explanations such as low self-control, lack of parental supervision, deviant peers, and neighborhood disadvantage. Moreover, virtually no research on the effect of a criminal record has examined the \"black box\" of mediating mechanisms or the consequence of arrest for postsecondary educational attainment. Analyzing longitudinal data with multiple and independent assessments of theoretically relevant domains, the authors estimate the direct effect of arrest on later high school dropout and college enrollment for adolescents with otherwise equivalent neighborhood, school, family, peer, and individual characteristics as well as similar frequency of criminal offending. The authors present evidence that arrest has a substantively large and robust impact on dropping out of high school among Chicago public school students. They also find a significant gap in four-year college enrollment between arrested and otherwise similar youth without a criminal record. The authors also assess intervening mechanisms hypothesized to explain the process by which arrest disrupts the schooling process and, in turn, produces collateral educational damage. The results imply that institutional responses and disruptions in students' educational trajectories, rather than social-psychological factors, are responsible for the arrest-education link.
Journal Article
Data-driven system to predict academic grades and dropout
Nowadays, the role of a tutor is more important than ever to prevent students dropout and improve their academic performance. This work proposes a data-driven system to extract relevant information hidden in the student academic data and, thus, help tutors to offer their pupils a more proactive personal guidance. In particular, our system, based on machine learning techniques, makes predictions of dropout intention and courses grades of students, as well as personalized course recommendations. Moreover, we present different visualizations which help in the interpretation of the results. In the experimental validation, we show that the system obtains promising results with data from the degree studies in Law, Computer Science and Mathematics of the Universitat de Barcelona.
Journal Article
A Statistical Analysis of Factors Affecting Higher Education Dropouts
One of the most significant indicators for assessing the quality of university careers is the dropout rate between the first and second year. Both literature on the subjects and the results that emerged from numerous specific investigations into the dropouts of the university system, showed the crucial importance of this junction between the first and the second year. Reasons for dropping out can be quite varied, ranging from incorrect and/or insufficient prospective student orientation, the willingness or need to find a job as quickly as possible, to a lack of awareness of not being able to cope with a particular course of study rather than another. In this paper we focus specifically on the problem of dropouts in Italy, addressing it from a dual point of view. At an aggregate level, the analysis deals with dropout rates in Italy between the first and second year, in order to identify the main trends and dynamics at the national level. Subsequently, we analyze individual-level data from the University of Bari Aldo Moro, aiming to identify the most important contributing factors. This individual-level approach has emerged over recent years, and is generally known as 'Educational Data Mining', focused on the development of ad hoc methods that can be used to discover regularities and new information within databases from contexts related to education. Using supervised classification methods, we are able to identify retrospectively the profile of students who are most likely to dropout.
Journal Article
Stressors and Turning Points in High School and Dropout: A Stress Process, Life Course Framework
High school dropout is commonly seen as the result of a long-term process of failure and disengagement. As useful as it is, this view has obscured the heterogeneity of pathways leading to dropout. Research suggests, for instance, that some students leave school not as a result of protracted difficulties but in response to situations that emerge late in their schooling careers, such as health problems or severe peer victimization. Conversely, others with a history of early difficulties persevere when their circumstances improve during high school. Thus, an adequate understanding of why and when students drop out requires a consideration of both long-term vulnerabilities and proximal disruptive events and contingencies. The goal of this review is to integrate long-term and immediate determinants of dropout by proposing a stress process, life course model of dropout. This model is also helpful for understanding how the determinants of dropout vary across socioeconomic conditions and geographical and historical contexts.
Journal Article
The Relationship Between School Dropout and Pregnancy Among Adolescent Girls and Young Women in South Africa
Background. Prevention of both school dropout and teen pregnancy represent clear public health priorities for South Africa, yet their complex and potentially cyclical relationship has not been fully explored. Objective. To further understand how this relationship operates, we analyzed data from a randomized trial of young women aged 13 to 20 years enrolled in school in rural South Africa to estimate the association between pregnancy and subsequent dropout and between dropout and subsequent pregnancy. Method. We examined inverse probability (IP) of exposure-weighted survival curves for school dropout by pregnancy and for pregnancy by school dropout. We used weighted curves to calculate 1-, 2-, and 3-year risk differences and risk ratios. Additionally, we used an IP-weighted marginal structural cox model to estimate a hazard ratio (HR) for each relationship. Results. Dropout from school was associated with subsequent pregnancy (HR 3.58; 95% confidence interval [CI] [2.04, 6.28]) and pregnancy was associated with subsequent school dropout (HR 2.36; 95% CI [1.29, 4.31]). Young women who attended school but attended fewer days had a higher hazard of pregnancy than those who attended more school (HR 3.64; 95% CI [2.27, 5.84]). Conclusion. Pregnancy is both a cause and a consequence of school dropout. Consideration of school attendance and academic performance could ultimately enhance pregnancy prevention efforts in this population. Programs should be tailored differently for (1) girls who have dropped out of school, (2) those who are in school and at risk for pregnancy, and (3) those who are in school and become pregnant.
Journal Article