A critical assessment of imbalanced class distribution problem: the case of predicting freshmen student attrition

Predicting student attrition is an intriguing yet challenging problem for any academic institution. Class-imbalanced data is a common in the field of student retention, mainly because a lot of students register but fewer students drop out. Classification techniques for imbalanced dataset can yield deceivingly high prediction accuracy where the overall predictive accuracy is usually driven by the majority class at the expense of having very poor performance on the crucial minority class. In this study, we compared different data balancing techniques to improve the predictive accuracy in minority class while maintaining satisfactory overall classification performance. Specifically, we tested three balancing techniques—oversampling, under-sampling and synthetic minority over-sampling (SMOTE)—along with four popular classification methods—logistic regression, decision trees, neuron networks and support vector machines. We used a large and feature rich institutional student data (between the years 2005 and 2011) to assess the efficacy of both balancing techniques as well as prediction methods. The results indicated that the support vector machine combined with SMOTE data-balancing technique achieved the best classification performance with a 90.24% overall accuracy on the 10-fold holdout sample. All three data-balancing techniques improved the prediction accuracy for the minority class. Applying sensitivity analyses on developed models, we also identified the most important variables for accurate prediction of student attrition. Application of these models has the potential to accurately predict at-risk students and help reduce student dropout rates

Parental Education, Grade Attainment & Earnings Expectations among University Students

While there is an extensive literature on intergenerational transmission of economic outcomes (education, health and income for example), many of the pathways through which these outcomes are transmitted are not as well understood. We address this deficit by analysing the relationship between socio-economic status and child outcomes in university, based on a rich and unique dataset of university students. While large socio-economic differences in academic performance exist at the point of entry into university, these differences are substantially narrowed during the period of study. Importantly, the differences across socio-economic backgrounds in university grade attainment for female students is explained by intermediating variables such as personality, risk attitudes and time preferences, and subject/college choices. However, for male students, we explain less than half of the socio-economic gradient through these same pathways. Despite the weakening socio-economic effect in grade attainment, a key finding is that large socio-economic differentials in the earnings expectations of university students persist, even when controlling for grades in addition to our rich set of controls. Our findings pose a sizable challenge for policy in this area as they suggest that equalising educational outcomes may not translate into equal labour market outcomes.Socio-Economic Status, Education, Inequality, Discrimination

Improving the Yields in Higher Education: Findings from Lumina Foundation's State-Based Efforts to Increase Productivity in U.S. Higher Education

In 2008, Lumina asked SPEC Associates (SPEC) to evaluate the foundation's grant making aimed at improving the productivity of higher education through statewide policy and program change. The initiative was initially known as Making Opportunity Affordable and later became known more broadly as Lumina's higher education productivity initiative. Eleven states received planning grants in 2008 and a year later seven of these states received multi-year grants to implement their productivity plans. In 2009, Lumina published Four Steps to Finishing First in Higher Education to frame the content of its productivity work. In 2010, the foundation, working with HCM Strategists, launched the Strategy Labs Network to deliver just-in-time technical assistance, engagement, informationsharing and convenings to states. Lumina engaged SPEC to evaluate these productivity investments in the seven states through exploring this over-arching question: What public will building, advocacy, public policy changes, and system or statewide practices are likely to impact higher education productivity for whom and in what circumstances, and which of these are likely to be sustainable, transferable, and/or scalable

Parental Education, Grade Attainment and Earnings Expectations among University Students

While there is an extensive literature on intergenerational transmission of economic outcomes (education, health and income for example), many of the pathways through which these outcomes are transmitted are not as well understood. We address this deficit by analysing the relationship between socio-economic status and child outcomes in university, based on a rich and unique dataset of university students. While large socio-economic differences in academic performance exist at the point of entry into university, these differences are substantially narrowed during the period of study. Importantly, the differences across socio-economic backgrounds in university grade attainment for female students is explained by intermediating variables such as personality, risk attitudes and time preferences, and subject/college choices. However, for male students, we explain less than half of the socio-economic gradient through these same pathways. Despite the weakening socio-economic effect in grade attainment, a key finding is that large socio-economic differentials in the earnings expectations of university students persist, even when controlling for grades in addition to our rich set of controls. Our findings pose a sizable challenge for policy in this area as they suggest that equalising educational outcomes may not translate into equal labour market outcomes.inequality, education, socio-economic status, discrimination

Digital learning resources and ubiquitous technologies in education

This research explores the educators' attitudes and perceptions about their utilisation of digital learning technologies. The methodology integrates measures from ‘the pace of technological innovativeness’ and the ‘technology acceptance model’ to understand the rationale for further ICT investment in compulsory education. A quantitative study was carried out amongst two hundred forty-one educators in Malta. It has investigated the costs and benefits of using digital learning resources in schools from the educator’s perspective. Principal component analysis has indicated that the educators were committed to using digital technologies. In addition, a step-wise regression analysis has shown that the younger teachers were increasingly engaging in digital learning resources. Following this study’s empirical findings educational stakeholders are better informed about how innovative technologies can support our students. In conclusion, this paper puts forward key implications and recommendations for regulatory authorities and policy makers for better curricula and educational outcomes.peer-reviewe

The Relationship Between Prior Experiences in Mathematics and Pharmacy School Success

Objective. To assess students’ pre-pharmacy math experiences, confidence in math ability, and relationship between experiences, confidence, and grades in math-based pharmacy courses. Methods. A cross-sectional survey of first year to third year pharmacy students was conducted. Students reported type of pre-pharmacy math courses taken, when they were taken [high school (HS) vs. college] and year of HS and college graduation. Students rated their confidence in math ability using the previously validated 11-item Fogerty Math Confidence Scale (Cronbach alpha=0.92). Math grade point average (GPA), Pharmacy College Admission Test quantitative (PCAT quant) scores, and grades (calculations and kinetics) were obtained from transcripts and school records. Spearman correlation and multivariate linear regression were used to compare math experiences, confidence, and grades. Results. There were 198 students who reported taking math courses 7.1 years since HS graduation and 2.9 years since their last schooling prior to pharmacy school. Students who took math courses with more time since HS/last schooling had lower calculations and kinetics grades. Students reporting having taken more HS math courses had better calculations grades. Students with higher math GPA, and PCAT quant scores also had higher calculations and kinetics grades. Greater confidence in math ability was associated with higher calculations grades. In multivariate regressions, PCAT quant scores and years since HS independently predicted calculations grades, and PCAT quant scores independently predicted kinetics grades. Conclusion. The number of pre-pharmacy math courses and time elapsed since they were taken are important factors to consider when predicting a pharmacy student’s success in math-based pharmacy school courses

Making it without losing it: Type A, achievement motivation, and scientific attainment revisited

In a study by Matthews et al. (1980), responses by academic psychologists to the Jenkins Activity Survey for Health Prediction, a measure of the Type A construct, were found to be significantly, positively correlated with two measures of attainment, citations by others to published work and number of publications. In the present study, JAS responses from the Matthews et al. sample were subjected to a factor analysis with oblique rotation and two new subscales were developed on the basis of this analysis. The first, Achievement Strivings (AS) was found to be significantly correlated with both the publication and citation measures. The second scale, Impatience and Irritability (I/I), was uncorrelated with the achievement criteria. Data from other samples indicate that I/I is related to a number of health symptoms. The results suggest that the current formulation of the Type A construct may contain two components, one associated with positive achievement and the other with poor health

Algorithmic Fairness from a Non-ideal Perspective

Inspired by recent breakthroughs in predictive modeling, practitioners in both industry and government have turned to machine learning with hopes of operationalizing predictions to drive automated decisions. Unfortunately, many social desiderata concerning consequential decisions, such as justice or fairness, have no natural formulation within a purely predictive framework. In efforts to mitigate these problems, researchers have proposed a variety of metrics for quantifying deviations from various statistical parities that we might expect to observe in a fair world and offered a variety of algorithms in attempts to satisfy subsets of these parities or to trade o the degree to which they are satised against utility. In this paper, we connect this approach to fair machine learning to the literature on ideal and non-ideal methodological approaches in political philosophy. The ideal approach requires positing the principles according to which a just world would operate. In the most straightforward application of ideal theory, one supports a proposed policy by arguing that it closes a discrepancy between the real and the perfectly just world. However, by failing to account for the mechanisms by which our non-ideal world arose, the responsibilities of various decision-makers, and the impacts of proposed policies, naive applications of ideal thinking can lead to misguided interventions. In this paper, we demonstrate a connection between the fair machine learning literature and the ideal approach in political philosophy, and argue that the increasingly apparent shortcomings of proposed fair machine learning algorithms reflect broader troubles faced by the ideal approach. We conclude with a critical discussion of the harms of misguided solutions, a reinterpretation of impossibility results, and directions for future researc