From access to attainment : patterns of social inequality and equity policies in higher education

Defence date: 14 December 2018Examining Board: Professor Fabrizio Bernardi, European University Institute (Supervisor); Professor Carlo Barone, Sciences Po Paris (Co-supervisor); Professor Juho Härkönen, European University Institute; Professor Mathieu Ichou, Institut national d’études démographiques-INEDChapter 6 'What works to reduce inequalities in higher education? A systematic review of the (quasi-)experimental literature on outreach and financial aid' is co-authored: Dr Koen Geven (30%) and Estelle Marie Régine Herbaut (70%)To what extent, and how, does social background influence students’ attainment in higher education? Building on the life course perspective on educational inequalities, this PhD thesis focuses on patterns of inequality formation in French higher education and on an evaluation of educational policies to reduce them. It assesses the effect of social origin on pivotal outcomes of higher education careers in both the vertical dimension of stratification (access to higher education, dropout) and horizontal dimension (access and transfer to prestigious institutions). In order to provide a comprehensive assessment of patterns of inequalities, from initial access to final attainment, this thesis further combines the study of single key transitions with an analysis of whole students’ trajectories during their educational careers. Focusing on policy solutions, it estimates the effect of alternative pathways on the composition of the student body in prestigious institutions and provides a systematic review of the (quasi-) experimental literature evaluating the impacts of both outreach interventions and financial aid on the outcomes of disadvantaged students in higher education. Results first confirm the crucial role of previous education in shaping social inequalities in higher education outcomes. However, these results also provide evidence of a “lingering” effect of social origin in the French higher education system for some crucial outcomes, especially in the horizontal dimension of social stratification. They further confirm the relevance of the compensatory advantage hypothesis in the formation of social inequalities in higher education outcomes, as, in France, socially advantaged students with lower performance are better able to gain eligibility to higher education and to overcome failure in their first year of tertiary studies. Finally, the systematic literature review allows the conclusion that some late interventions, when well-designed, are efficient in increasing opportunities for disadvantaged students and reducing inequalities in higher education outcomes. Most notably, outreach interventions which complement information with personalized support are usually efficient in increasing access rates, and need-based grants appear to raise, often substantially, the graduation rates of disadvantaged students. Finally, the implications of these results for our understanding of social stratification in higher education and some promising avenues for future research are discussed

A Mixed Methods Analysis of School- and Student-Level Effects: Mathematics Course Completion and Achievement Beyond Algebra 2 Among Mexican American Female High School Students

In the United States, as the Hispanic population continues to grow, persistently low mathematics achievement among Mexican Americans continues to exist, particularly among girls. Low mathematics achievement places this group in a disadvantaged position. As such, a multistage mixed method study was implemented to investigate possible factors associated with mathematics achievement and the probability of taking mathematics courses beyond Algebra 2 among Mexican American female high school students. The Educational Longitudinal Study (ELS:2002) database provided the quantitative sample (n = 710) of respondents who self-identified as Mexican American, female, and had a math IRT score. A parallel sample (n = 5), of college age women attending a public university in the southeast United States, provided qualitative data through face-to-face interviews. Inclusion criteria for the parallel sample was female, Mexican descendent self-identifying as first, second or third generation and completion of high school math credits beyond Algebra 2. Methods for analysis were the three-dimensional space narrative structure for the qualitative data and multilevel analysis for the quantitative data. Outcome variables were mathematics achievement and math credits beyond Algebra 2. Explanatory variables included in the model for the student level were social economic status, generational status, sense of belonging, parent expectation to earn at least a Bachelor’s degree, homework rules, a measure of math self-efficacy, number of advanced placement math credits, and seeing school counselor for college advice. Explanatory variables for the school level included teacher encouragement, percentage of Hispanic teachers, and percentage of minority students. Findings indicated significant student effects for math self-efficacy, seeing the school counselor, and advanced placement math credits, when using the imputed model with math achievement as the outcome variable. Parental expectation to graduate college was significant when using math credits earned beyond Algebra 2 as the outcome variable. Qualitative data provided insights about participants enjoying opportunities for hands-on projects, working in groups, and solving math problems. Participants shared that teachers served as role models and that parents expected them to graduate from college. The qualitative data provides guidance for including sense of belonging and parental educational levels with further research relative to Mexican American female students

A Combined Approach of Process Mining and Rule-based AI for Study Planning and Monitoring in Higher Education

This paper presents an approach of using methods of process mining and rule-based artificial intelligence to analyze and understand study paths of students based on campus management system data and study program models. Process mining techniques are used to characterize successful study paths, as well as to detect and visualize deviations from expected plans. These insights are combined with recommendations and requirements of the corresponding study programs extracted from examination regulations. Here, event calculus and answer set programming are used to provide models of the study programs which support planning and conformance checking while providing feedback on possible study plan violations. In its combination, process mining and rule-based artificial intelligence are used to support study planning and monitoring by deriving rules and recommendations for guiding students to more suitable study paths with higher success rates. Two applications will be implemented, one for students and one for study program designers.Comment: 12 pages, 4 figures, conference, 30 reference

Stories of young dropouts: a social survey of success and failure

info:eu-repo/semantics/publishedVersio

Informing the Debate: Comparing Boston's Charter, Pilot and Traditional Schools

Assesses the impact of charter and pilot schools on achievement by tracking students who showed similar academic traits in earlier grades across school types. Also compares applicants who won the lottery to attend charters or pilots and those who did not

The Effect of Transitions on Access to Higher Education

Explores how the transition process from secondary to higher education affects access to higher education in the Netherlands, South Africa, Ukraine, and the United States. Examines barriers and makes recommendations for expanding "access with success.

The Road Map Project: 2014 Results Report

The Road Map Project's annual report card shows data on 29 indicators of student success, which are important measures related to student achievement from cradle through college. Data in the report are often disaggregated by district, student race/ethnicity or income level to illustrate the region's challenges and progress.The Road Map Project is a region-wide collective impact effort aiming to dramatically improve education results in South King County and South Seattle, the county's areas of greatest need. The project's goal is to double the number of students who are on track to graduate from college or earn a career credential by 2020, and to close opportunity gaps. Seven school districts -- Auburn, Kent, Federal Way, Highline, Renton, Seattle (south-end only) and Tukwila -- are among the hundreds of partners working together toward the Road Map Project's 2020 goal. The 2014 results report includes a special focus on whether the region is on track to reach the goal