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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.

Predicting school dropout rates is an important issue for the smooth execution of an educational system. This problem is solved by classifying students into two classes using educational activities related statistical datasets. One of the classes must identify the students who have the tendency to persist. The other class must identify the students who have the tendency to dropout. This problem often encounters a phenomenon that masks out the obtained results. This study delves into this phenomenon and provides a reliable educational data mining technique that accurately predicts the dropout rates. In particular, the three data classifying techniques, namely, decision tree, neural networks and Balanced Bagging, are used. The performances of these classifies are tested with and without the use of a downsample, SMOTE and ADASYN data balancing. It is found that among other parameters geometric mean and UAR provides reliable results while predicting the dropout rates using Balanced Bagging classifying techniques.

This study examined the association between school suspension rates and dropout rates in a statewide sample of 289 Virginia public high schools. The contribution of suspension rates on dropout rates was examined for both Black and White students, after controlling for school demographics (school racial composition, percentage of students eligible for Free and Reduced Price Meals, urbanicity), and school resources (per pupil expenditure). Because student attitudes also might influence suspension rates, the prevalence of aggressive attitudes and rejection of school rules among students were used as additional predictors. Hierarchical regression analyses using schools as the unit of analysis found that, after entering both school demographics and student attitude measures, schools with high suspension rates tended to have high dropout rates. There were comparable findings for both White and Black students, although school suspension rates were more strongly associated with White dropout rates than Black dropout rates. These findings contribute new evidence that suspension policies may have an adverse effect on student completion of high school.

Art appreciation is highly subjective. Fraiberger et al. used an extensive record of exhibition and auction data to study and model the career trajectory of individual artists relative to a network of galleries and museums. They observed a lock-in effect among highly reputed artists who started their career in high-prestige institutions and a long struggle for access to elite institutions among those who started their career at the network periphery. Science , this issue p. 825 Institutional networks play key roles in guiding careers when quality cannot be measured objectively. In areas of human activity where performance is difficult to quantify in an objective fashion, reputation and networks of influence play a key role in determining access to resources and rewards. To understand the role of these factors, we reconstructed the exhibition history of half a million artists, mapping out the coexhibition network that captures the movement of art between institutions. Centrality within this network captured institutional prestige, allowing us to explore the career trajectory of individual artists in terms of access to coveted institutions. Early access to prestigious central institutions offered life-long access to high-prestige venues and reduced dropout rate. By contrast, starting at the network periphery resulted in a high dropout rate, limiting access to central institutions. A Markov model predicts the career trajectory of individual artists and documents the strong path and history dependence of valuation in art.

Although online learning is expanding in availability and popularity, the high dropout rates remain a challenging problem. In this paper, we reviewed the existing empirical studies on online course dropouts in post-secondary education that were published during the last 10 years. We identified 69 factors that influence students' decisions to dropout and classified them into three main categories: (a) Student factors, (b) Course/Program factors, and (c) Environmental factors. We then examined the strategies proposed to overcome these dropout factors: (a) understanding each student's challenges and potential, (b) providing quality course activities and well-structured supports, and (c) handling environmental issues and emotional challenges. Finally, we discussed issues regarding dropout factors and strategies for addressing these factors and offered recommendations for future research.

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.

Massive open online courses (MOOCs) are among the latest e-learning initiative that have gained a wide popularity among many universities. Student dropout in MOOCs is a major concern in the higher education and policy-making communities. Most student dropout is caused by factors outside the institution’s control. In this study, a multiple-criteria decision-making method was used to identify the core factors and possible causal relationships responsible for the high dropout rate in MOOCs. Twelve factors, distributed across four dimensions, related to students’ dropout from online courses were identified from the literature. Then, a total of 17 experienced instructors in MOOCs from different higher education institutions were invited to assess the level of influence of these factors on each other. The results identified six core factors that directly influenced student dropout in MOOCs, these were: academic skills and abilities, prior experience, course design, feedback, social presence, and social support. Other factors such as interaction, course difficulty and time, commitment, motivation, and family/work circumstances were found to play a secondary role in relation to student dropout in MOOCs. The causal relationships between the primary and secondary factors were mapped and described. Outcomes from this study can offer the necessary insights for educators and decision makers to understand the cause–effect relationships between the factors influencing MOOC student dropout, thus providing relevant interventions in order to reduce the high dropout rate.

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.

Student dropout can be conceptualized as a decision-making process, consisting of different phases. Based on previous literature on student dropout, decision-making, and action-phases, we proposed that the process of developing dropout intentions includes the following phases: non-fit perception , thoughts of quitting/changing, deliberation , information search , and a final decision . In the present cross-sectional study, we empirically investigated if the assumed phases can be distinguished from each other, if the phases follow the presumed order, and whether each phase is associated with certain characteristics. Furthermore, we considered a strict separation between quitting studies completely and changing a major. For this purpose, we analyzed data of N = 1005 students (average age of 23.0 years; 53% female; 47% male) from a German University. By using confirmatory factor analyses, we found the supposed factor structure for the different phases concerning both kinds of dropout, quitting studies, and changing majors. In each process, structural equation modelling indicated positive relations between adjoining phases. The factor values correlated to a substantial amount with an assortment of variables associated with student dropout. On a theoretical level, the conception of different phases of student dropout helps to get a better understanding of regulatory processes in the context of student dropout.

Despite the increasing popularity of doctoral education, many students do not complete their studies, and very little information is available about them. Understanding why some students consider that they do not want to, or cannot, continue with their studies is essential to reduce dropout rates and to improve the overall quality of doctoral programmes. This study focuses on the motives students give for considering dropping out of their doctoral degree. Participants were 724 social sciences doctoral students from 56 Spanish universities, who responded to a questionnaire containing doctoral degree conditions questions and an open-ended question on motives for dropping out. Results showed that a third of the sample, mainly the youngest, female and part time students, stated that they had intended to drop out. The most frequent motives for considering dropping out were difficulties in achieving a balance between work, personal life and doctoral studies and problems with socialization. Overall, results offer a complex picture that has implications for the design of doctoral programmes, such as the conditions and demands of part-time doctoral studies or the implementation of educational proposals that facilitate students' academic and personal integration into the scientific community in order to prevent the development of a culture of institutional neglect.