Explore the vast range of titles available.

As in most countries worldwide, Sub-Saharan African countries are striving to build their human capital so they can compete for jobs and investments in an increasingly globalized world. In this region, which includes the largest number of countries that have not yet attained universal primary schooling, the ambitions and aspirations of Sub-Saharan African countries and their youth far exceed this basic goal. Over the past 20 years, educational levels have risen sharply across Sub-Saharan Africa. Already hard at work to provide places in primary schools for all children, most countries of the region are also rapidly expanding access to secondary and tertiary levels of education. Alongside this quantitative push is a growing awareness of the need to make sure that students are learning and acquiring the skills needed for life and work. Achieving education of acceptable quality is perhaps an even greater challenge than providing enough school places for all. Thus, Sub-Saharan African countries are simultaneously confronting many difficult challenges in the education sector, and much is at stake. This book gives those concerned with education in Sub-Saharan Africa an analysis of the sector from a cross-country perspective, aimed at drawing lessons that individual country studies alone cannot provide. A comparative perspective is useful not only to show the range of possibilities in key education policy variables but also to learn from the best performers in the region. (Although the report covers 47 Sub- Saharan African countries whenever possible, some parts of the analysis center on the region's low-income countries, in particular, a sample of 33 low-income countries). Although countries ultimately must make their own policy choices and decide what works best in their particular circumstances, Sub-Saharan African countries can benefit from learning about the experiences of other countries that are faced with, or have gone through, similar development paths. Given the large number of countries included in the analysis, the book finds that Sub-Saharan African countries have more choices and more room for maneuver than will appear if attention were focused on only one or a few country experiences. Countries can make better choices when understanding the breadth of policy choices available to them. They are well advised, however, to evaluate the applicability of policy options to their contexts and to pilot and evaluate the results for performance and subsequent improvement.

Background: Despite the established efficacy of psychological therapies for post-traumatic stress disorder (PTSD) there has been little systematic exploration of dropout rates. Objective: To ascertain rates of dropout across different modalities of psychological therapy for PTSD and to explore potential sources of heterogeneity. Method: A systematic review of dropout rates from randomized controlled trials (RCTs) of psychological therapies was conducted. The pooled rate of dropout from psychological therapies was estimated and reasons for heterogeneity explored using meta-regression. Results:: The pooled rate of dropout from RCTs of psychological therapies for PTSD was 16% (95% CI 14-18%). There was evidence of substantial heterogeneity across studies. We found evidence that psychological therapies with a trauma-focus were significantly associated with greater dropout. There was no evidence of greater dropout from therapies delivered in a group format; from studies that recruited participants from clinical services rather than via advertisements; that included only military personnel/veterans; that were limited to participants traumatized by sexual traumas; that included a higher proportion of female participants; or from studies with a lower proportion of participants who were university educated. Conclusions: Dropout rates from recommended psychological therapies for PTSD are high and this appears to be particularly true of interventions with a trauma focus. There is a need to further explore the reasons for dropout and to look at ways of increasing treatment retention.

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.

Virtual reality exposure therapy (VRET) is currently being used to treat social anxiety disorder (SAD); however, VRET's magnitude of efficacy, duration of efficacy, and impact on treatment discontinuation are still unclear. We conducted a meta-analysis of studies that investigated the efficacy of VRET for SAD. The search strategy and analysis method are registered at PROSPERO (#CRD42019121097). Inclusion criteria were: (1) studies that targeted patients with SAD or related phobias; (2) studies where VRET was conducted for at least three sessions; (3) studies that included at least 10 participants. The primary outcome was social anxiety evaluation score change. Hedges' g and its 95% confidence intervals were calculated using random-effect models. The secondary outcome was the risk ratio for treatment discontinuation. Twenty-two studies (n = 703) met the inclusion criteria and were analyzed. The efficacy of VRET for SAD was significant and continued over a long-term follow-up period: Hedges' g for effect size at post-intervention, -0.86 (-1.04 to -0.68); three months post-intervention, -1.03 (-1.35 to -0.72); 6 months post-intervention, -1.14 (-1.39 to -0.89); and 12 months post-intervention, -0.74 (-1.05 to -0.43). When compared to in vivo exposure, the efficacy of VRET was similar at post-intervention but became inferior at later follow-up points. Participant dropout rates showed no significant difference compared to in vivo exposure. VRET is an acceptable treatment for SAD patients that has significant, long-lasting efficacy, although it is possible that during long-term follow-up, VRET efficacy lessens as compared to in vivo exposure.

The Italian university system has long been characterised by high non-completion rates, though aggregate data show a slight reduction of dropouts in recent years. The most straightforward theoretical explanation for this lies in the lowering opportunity cost of studying due to the financial and economic crisis. Nonetheless, this interpretation is likely to be partly misleading. Indeed, when the crisis hit Italy, enrolment rates had been declining for years and the sample of freshmen has become increasingly selected according to family 'social class', family cultural background, type of high school diploma and individual ability. Since a good family background, as well as other individual characteristics, significantly increases students' probability of succeeding, the recent decline in dropout rates could partly depend on sample selection. By applying probit selection models and decomposition techniques to a sample of Italian university students enrolled in different periods of time, I find that changes in students' background and students' characteristics play a major role in the recent reduction of the aggregate dropout rate. (HRK / Abstract übernommen).

Studies of student risk of school dropout have shown that present predictors of at-risk status do not accurately identify a large percentage of students who eventually drop out. 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, the author extends past longitudinal conceptions of dropout to a longitudinal risk perspective, using survival analysis, life tables, and discrete-time hazard modeling to appropriately account for student graduation, transfer, or dropout. The risk of dropout began in Grade 7, with the most hazardous years at Grades 8 and 11. A novel calculation of teacher-assigned grades, noncumulative GPA, is identified as a strong predictor of student dropout.

Distance Learning has enabled educational practices based on digital platforms, generating massive amounts of data. Several initiatives use this data to identify dropout contexts, mainly providing teacher support about student behavior. Approaches such as Active Methodologies are known as having good potential to involve and motivate students. This article presents a systematic mapping aiming to identify current Educational Data Mining and Learning Analytics methods. Besides, we identify Active Methodologies’ application to mitigate dropout in Distance Learning. We evaluated 668 papers published from January 2015 to March 2020. The results indicate a growing application of Educational Data Mining and Learning Analytics to identify and mitigate students’ abandonment in Distance Learning. However, studies with Active Methodologies to minimize dropout and enhance student permanence are scarce. Some works suggest Active Methods as a possible complement of Learning Analytics in dropout.

Data mining is widely considered a powerful instrument for searching and acquiring essential relationships among different variables/attributes in a database. Data mining applied in the educational framework is referred to as educational data mining (EDM). EDM enables to get insights into various higher education phenomena, such as students’ academic paths, learning behaviours and determinants of academic success or dropout. In this paper, we aim at evaluating the usefulness of a particular latent class model, the Bayesian Profile Regression, for the identification of students more likely to drop out. Considering students’ performance, motivation and resilience, this technique allows to draw the profiles of students with a higher risk of academic failure. The working example is based on real data collected through an online questionnaire filled in by undergraduate students of an Italian University.

John Tyler and Magnus Lofstrom take a close look at the problems posed when students do not complete high school. The authors begin by discussing the ongoing, sometimes heated, debate over how prevalent the dropout problem is. They note that one important reason for discrepancies in reported dropout rates is whether holders of the General Educational Development (GED) credential are counted as high school graduates. The authors also consider the availability of appropriate student data. The overall national dropout rate appears to be between 22 and 25 percent, but the rate is higher among black and Hispanic students, and it has not changed much in recent decades. Tyler and Lofstrom conclude that schools are apparently doing about as well now as they were forty years ago in terms of graduating students. But the increasingly competitive pressures associated with a global economy make education ever more important in determining personal and national well-being. A student's decison to drop out of school, say the authors, is affected by a number of complex factors and is often the culmination of a long process of disengagement from school. That decision, not surprisingly, carries great cost to both the student and society. Individual costs include lower earnings, higher likelihood of unemployment, and greater likelihood of health problems. Because minority and low-income students are significantly more likely than well-to-do white students to drop out of school, the individual costs fall unevenly across groups. Societal costs include loss of tax revenue, higher spending on public assistance, and higher crime rates. Tyler and Lofstrom go on to survey research on programs designed to reduce the chances of students' dropping out. Although the research base on this question is not strong, they say, close mentoring and monitoring of students appear to be critical components of successful programs. Other dropout-prevention approaches associated with success are family outreach and attention to students' out-of-school problems, as well as curricular reforms. The authors close with a discussion of second-chance programs, including the largest such program, the GED credential.

Dropping out from undergraduate medical education is costly for students, medical schools, and society in general. Therefore, the early identification of potential dropout students is important. The contribution of personal features to dropout rates has merited exploration. However, there is a paucity of research on aspects of student experience that may lead to dropping out. In this study, underpinned by theoretical models of student commitment, involvement, and engagement, we explored the hypothesis of using inferior participation as an indicator of a higher probability of dropping out in year 1. Class participation was calculated as an aggregate score based on teachers’ daily observations in class. The study used a longitudinal dataset of six cohorts of high-school entry students (N = 709, 67% females) in one medical school with an annual intake of 120 students. The findings confirmed the initial hypothesis and showed that lower scores of class participation in year 1 added predictive ability to pre-entry characteristics (Pseudo- R 2 raised from 0.22 to 0.28). Even though the inclusion of course failure in year 1 resulted in higher explanatory power than participation in class (Pseudo- R 2 raised from 0.28 to 0.63), ratings of class participation may be advantageous to anticipate dropout identification, as those can be collected prior to course failure. The implications for practice are that teachers’ ratings of class participation can play a role in indicating medical students who may eventually drop out. We conclude that the scores of class participation can contribute to flagging systems for the early detection of student dropouts.