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This paper investigates the effect of student loans on students' (financial) behavior. For causal identification, we exploit quasi-experimental evidence using a nudge in the take-up of student loans in higher education in the Netherlands. We estimate an instrumental variable (IV) model with a first-stage Difference-in-Differences design. We find that a decline in the default student loan reduced monthly student borrowing by 141 euros. A one-euro decline in student loans reduced students' expenditures by 61 cents, but also led to a substantial increase of parental financial contributions (43 cents). Especially expenditures on leisure activities were affected. There is no evidence for increased labor earnings among students, on average. Self-reported indicators of academic performance do not worsen in response to the reform; students' GPA even improves. [ABSTRACT FROM AUTHOR]

• We examine the implications of college admissions criteria on students' outcomes. • Examine admissions at a large university with two admission tracks. • Regular admission track is based exclusively on academic performance while discretionary track considers non-academic qualities. • Discretionary admission students enjoy significantly higher earnings compared to marginal group of regular admission students. • Earnings advantage persists up to three years after graduation. This paper examines the implications of college admissions criteria on students' academic and non-academic performance in university and their labor market outcomes. We exploit a unique feature of the admissions system at a large university in Singapore, the National University of Singapore, that has two admission tracks – a regular admission track where admission is based exclusively on academic performance and a discretionary admission (DA) track where applicants can instead gain admission on the basis of demonstrated non-academic qualities. Comparing students admitted through each track, we find that DA students fare similarly in terms of academic performance in university as marginal students admitted through the regular route. However, they are significantly more likely to be involved in optional academic and non-academic college activities and earn substantially higher labor market earnings up to three years after graduation. These results are not driven by the DA process differentially selecting students on the basis of family background or unobserved academic ability. [ABSTRACT FROM AUTHOR]

Many students who enter college are insufficiently prepared to follow a demanding college-level curriculum. Thus, higher education institutions often require low-performing students to repeat failed courses, a full term, or even a full year. This paper is the first to investigate the effects of such a “(grade) retention” policy on student performance in higher education. We study a setting where first-year undergraduates who fall short of a pre-defined performance requirement have to repeat all first-year courses before they can proceed to the second year. To determine the causal effect of retention and repetition on student performance, we apply a sharp regression discontinuity design to administrative data from a Swiss university. Based on a sample of 5000 students, we find that grade retention increases dropout probabilities after the first year by about 10 percentage points. Repetition of a full year persistently boosts grade point averages by about 0.5 standard deviations, but does not affect study pace and major choices. [ABSTRACT FROM AUTHOR]

Community colleges play a major role in postsecondary education, yet previous research has emphasized the impact of merit aid on four-year students rather than two-year students. Furthermore, researchers have focused on the impact of merit aid on enrollment and outcomes during college, but to my knowledge, none have yet considered the impact of aid on earnings after college. This paper utilizes discontinuities in eligibility criteria for a large merit scholarship to examine the local impact of aid on student outcomes both during college and after college. The findings suggest that reducing the cost of community college does not impact persistence, academic performance, degree completion, expected earnings, or short-term earnings after college for marginally eligible students. [ABSTRACT FROM AUTHOR]

Abstract: Undergraduate study behaviours, principally lecture attendance and additional study, are shown to predict better student achievement by many researchers. Despite this, there is not much evidence on the determinants of these behaviours. This is the first paper to explore the determinants of study behaviours across multiple subject areas; and is the first to incorporate students’ noncognitive traits into such a model; that the authors are aware of. This enables the formation of policy that can improve academic achievement by encouraging study behaviour. The results show that students’ noncognitive traits, in particular conscientiousness and future-orientation, are important determinants of lecture attendance and additional study hours. In fact, there is very little that explains undergraduate study behaviour besides noncognitive traits. Standard economic factors, such as family income, financial aid and parental transfers, are not predictive of study behaviours. Some comments are provided on a potential behavioural economics approach to encouraging study behaviours. [Copyright &y& Elsevier]

• Academic institutions need to be more efficient in supporting students at risk. • Automated machine learning helps in identifying optimal prediction models. • Ensemble models with balanced data enhances prediction using pre-admission data. Students' success has recently become a primary strategic objective for most institutions of higher education. With budget cuts and increasing operational costs, academic institutions are paying more attention to sustaining students' enrollment in their programs without compromising rigor and quality of education. With the scientific advancements in Big Data Analytics and Machine Learning, universities are increasingly relying on data to predict students' performance. Many initiatives and research projects addressed the use of students' behavioral and academic data to classify students and predict their future performance using advanced statistics and Machine Learning. To allow for early intervention, this paper proposes the use of Automated Machine Learning to enhance the accuracy of predicting student performance using data available prior to the start of the academic program. Image, graphical abstract [ABSTRACT FROM AUTHOR]