Student loans occupy a significant portion of the federal budget, as well as, the largest financial burden in terms of debt for graduates. This paper explores data-driven approaches towards understanding the repayment of such loans. Using statistical and machine learning models on the College Scorecard Data, this research focuses on extracting and identifying key factors affecting the repayment of a student loan. The specific factors can be used to develop models which provide predictive capability towards repayment rate, detect irregularities non-repayment, and help understand the intricacies of student loans.
Other studies @cite_14 have found that nonprofit or public four year colleges have higher repayment rate than for-profit and two-year colleges. Repayment rates may also be affected by geographic area, as institution located in rural areas @cite_12 . In the work done by Chen @cite_17 , gender has also been found to be one of the factors. Different studies done in the domain report disparate factors associated with the repayment of student loans without any consensus among the reported results. The observations of inconsistencies of the factors in the studies can attributed to the different analytical methodologies, and the disparate datasets the methods were applied to.
@cite_14 @cite_12 @cite_17
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The federal government holds colleges accountable if too many of their students default on loan repayment, but the default measure traditionally used captures only a fraction of students who are struggling to repay their loans. The 2015 College Scorecard dataset introduced a new loan repayment metric, showing that the percentage of students who have not reduced the principal balance of their loans by at least $1 over a given period of time far outstrips the traditional loan default measure. Using a sample of 3,595 colleges, we test the extent to which student demographics, institutional characteristics, and state-level economic factors are associated with repayment rates and default rates. We also examine whether factors associated with loan repayment rates change between one and seven years after students begin repayment. We find that characteristics traditionally associated with economic disadvantage, including being a first-generation college student or a member of an underrepresented minority group, t... This study updates and expands the literature on student loan default. By applying multilevel regression to the Beginning Postsecondary Students survey, four key findings emerge. First, attending proprietary institutions is strongly associated with default, even after accounting for students’ socioeconomic and academic backgrounds. Second, cumulative loan debt has a non-linear relationship to defaulting. Third, minoritized and students from low-income families default at disproportionately high rates; and fourth, unemployment and degree completion are strongly associated with greater default rates. These findings counter the argument that default is a “preexisting condition,” and the discussion is framed around implications for federal financial aid policy. This article examines debt burden among college graduates and contributes to previous research by incorporating institutional and state characteristics. Utilizing a combination of national datasets and zero-one inflated beta regression, we find several major themes. First, family income and college experiences are strongly associated with the probability of zero debt burden as well as the level of debt burden. Second, graduates from private institutions have a higher level of debt burden than graduates from a public institution. Lastly, state funding of merit-based aid programs play a role in reducing students’ debt burden, but the effect disappears when accounting for the “Georgia effect.” Conclusions and suggestions are made about the roles institutions and government can play in reducing debt burden among college graduates. T his document is the report of a workshop that convened a group of experts to discuss the recruitment and retention of women in Computer Science and Engineering (CSE) Graduate Programs. (1) Participants included long-time members of the CSE academic and research communities, social scientists engaged in relevant research, and directors of successful retention efforts. (2) The report is a compendium of the experience and expertise of workshop participants, rather than the result of a full-scale, scholarly study into the range of issues. Its goal is to provide departments with practical advice on recruitment and retention in the form of a set of specific recommendations. Women are significantly underrepresented in CSE academic departments [5][40]. As computing technology becomes increasingly pervasive, this underrepresentation translates into a loss of opportunity for individuals, a loss of talent to the workforce, and a loss of creativity in shaping the future of society. While there are many causes of this underrepresentation some rooted in early socialization and primary educational experiences academic departments at the university level nevertheless can have an effect [6][7]. In particular, an improvement at the graduate level in recruitment and retention (and thus in graduation rates) would enable more women to move into visible and influential positions in the CSE community. The increasing presence of these women would provide positive role models and mentors. In order to treat all students fairly, educators must pay attention to gender-based traits. Although in characterizing behaviors one must be careful to acknowledge the existence of individual differences and to avoid stereotyping, there is a large body of information on gender traits. There is strong evidence, for example, that women, even though they perform at the same levels, have less confidence in their abilities and individual accomplishments than men [2][17][36][39][43]. Women are often less aggressive than male students in promoting themselves, attempting new or challenging activities, and pursuing awards or fellowships. There is evidence that females come to computing as only one interest among many, and are thus less single-minded than their male counterparts [27]. Often women report feeling "out of place" in the male-dominated, hacker culture [3] [22] [28]. In light of such differences, some of our recommendations are gender-specific. Most, however, are not. The adoption of our recommendations would improve the educational environment for all students. The recommendations are given in two sections, the first