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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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