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English Corequisite Remediation Improves Students’ Early Course Progression Outcomes but Does Not Increase Persistence Rates

Trey Miller, Lindsay Daugherty, Paco Martorell, and Russell Gerber

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What are corequisites?

Faced with troubling evidence on the success of students who take traditional developmental education (DE) courses, states and higher education institutions across the United States are rethinking the way they address college readiness. Corequisite remediation is one promising and common approach to DE reform. Under corequisite remediation, students skip the traditional DE course(s) and move immediately into a foundational college-level course, while also being required to enroll in concurrent DE support in that same semester. Corequisites also call for changes to instruction to better align content in DE with college-level coursework and some models build in opportunities for more personalized support and/or peer support through various design features such as smaller class sizes and the mixing of college ready and DE students.

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Comprehensive College Transition Program Increases Students’ Psychosocial Outcomes

Tatiana Melguizo, Paco Martorell, Elise Swanson, W. Edward Chi, Elizabeth Park, & Adrianna Kezar

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Is there an added benefit of providing college students with comprehensive support in addition to a scholarship?

An experimental evaluation of the Thompson Scholars Learning Communities (TSLC) program in the University of Nebraska system finds that participating in a comprehensive college transition program increases students’ sense of belonging (e.g. feeling part of the institution) and feelings of mattering (e.g. feeling they are valued on campus) relative to their peers. We find some evidence to suggest that program may be equity enhancing, given large observed increases in feelings of mattering among traditionally underserved students. We focus on the impact of the program on four key psychosocial outcomes that provide insight into students’ experiences on campus and help capture a broader understanding of student success than purely academic outcomes. Our findings suggest that comprehensive programs can improve students’ psychosocial outcomes.

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Examining the Earnings Trajectories of Community College Students Using a Piecewise Growth Curve Modeling Approach

Summary by: Lily An

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Traditional methods of estimating the returns to community college remain imprecise.

Historically, to estimate the labor market returns to a community college degree, researchers have compared the earnings of students who completed a degree to those who did not, at a single point in time, while controlling for background characteristics. With the expansion of longitudinal data sets, researchers have begun to consider how earnings before and during community college can affect returns to community college. However, even improved econometric analyses overlook some temporal influences on predicted earnings growth, such as the time between graduation and measured earnings, instead estimating averaged returns over time. These influences are particularly salient for community college students, who vary in their time-to-degree completion and often enter college with pre-existing or concurrent work experiences.

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Early College, Continued Success: Longer-Term Impact of Early College High Schools

Mengli Song, Kristina L. Zeiser, Drew Atchison, and Iliana Brodziak de los Reyes

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What are Early College High Schools?

Early Colleges are small high schools designed to increase the opportunity for students–particularly students traditionally underrepresented in higher education­–to earn a postsecondary credential. To achieve this goal, they partner with colleges and universities to provide high school students with college experience with the expectation that all students will earn an associate’s degree or up to 2 years of college credits during high school at no or low-cost to their families. Early Colleges also provide a rigorous and supportive high school environment to help students navigate and succeed in college coursework.

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The Methodological Challenges of Measuring Institutional Value-added in Higher Education

Tatiana Melguizo, Gema Zamarro, Tatiana Velasco, and Fabio J. Sanchez

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Assessing the quality of higher education is hard but there is growing pressure for governments to create a ranking system for institutions that can be used for assessment and funding allocations.  Such a system, however, would require a reliable methodology to fairly assess colleges using a wide variety of indicators. Countries with centralized governance structures have motivated researchers to develop “value-added” metrics of colleges’ contributions to student outcomes that can be used for summative assessment (Coates, 2009; Melguizo & Wainer, 2016; Shavelson et al. 2016). Estimating the “value-added” of colleges and programs, however, is methodologically challenging: first, high- and low-achieving students tend to self-select into different colleges– a behavior that if not accounted for, may yield to estimates that capture students’ prior achievement rather than colleges’ effectiveness at raising achievement; second, measures considering gains in student learning outcomes (SLOs) as indicators at the higher education level are scant. In our paper, we study these challenges and compare the methods used for obtaining value-added metrics in the context of higher education in Colombia.

How to best estimate value-added models in higher education?

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The Higher Education Enrollment Decision: Feedback on Expected Study Success and Updating Behavior

Chris van Klaveren, Karen Kooiman, Ilja Cornelisz & Martijn Meeter

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Secondary school students tend to be overly optimistic about how well they will perform in college. This overconfidence leads to suboptimal decision making. But what if secondary school students were told their likelihood of succeeding in the college program they applied to prior to their decision to enroll?  Would this influence their decision to enroll?

This study presents the results of a field experiment in which a random half of 313 secondary-school students applying to higher education received personalized predictions on study success (the other half did not receive such predictions). A comparison of the enrolment rates of the two groups of students helps us understand the effect of receiving these personalized predictions. We find that:

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The Implications of Teacher Selection and the Teacher Effect in Individually Randomized Group Treatment Trials

Michael Weiss

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Beware! Teacher effects could mess up your individually randomized trial! Or such is the message of this paper focusing on what happens if you have individual randomization, but teachers are not randomly assigned to experimental groups.

The key idea is that if your experimental groups are systematically different in teacher quality, you will be estimating a combined impact of getting a good/bad teacher on top of the impact of your intervention.

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