In this study, we investigated the currently applied selective admission criteria and tools of the two-year research master's programs of both the Graduate Schools of Life Sciences and Natural Sciences of Utrecht University (the Netherlands). In addition, we evaluated their transparency to applicants. Both admissions staff members and applicants participated. To determine admission criteria that are important for admission decisions, we ranked 51 admission criteria and, on their basis, combined into six domains: academic background, grades, cognitive ability, research background, personality and personal competencies, motivation factors. To evaluate transparency, we contrasted the perceptions of applicants with the actual importance of admission criteria, as reported by admission staff members. We found that admissions criteria related to personality and personal competencies are less important in admission decisions than criteria related to grades, academic background and motivation. The applicants find the admissions decisions transparent to a moderate degree. This study also revealed that selectors use criteria and tools both with and without predictive value for later graduate performance. Moreover, some of the currently applied admission instruments might be prone to admission biases. We advocate selectors to use admission criteria and tools that are evidence-based, resistant to admission biases, and transparent to the applicants.

Over the last three decades, policy-makers have developed numerous measures, policies, projects and programs with the intention to increase the enrolment and participation of underrepresented groups, however, little is known about the ways in which such initiatives shape opportunities for potential students. Knowing which of these initiatives work and whether they are achieving their intended goals is of utmost importance for policy-makers across Europe. This paper aims to collect, document, scrutinize and critically analyze the current research literature which assesses the effectiveness of different public initiatives at Higher Education Institutions’ (HEIs) level for widening access for underrepresented groups and, at the same time, to identify gaps and make recommendations for potential further research. The 17 identified studies can be categorized based on the access measures they analyze: (1) outreach, counselling and mentoring of prospective students; (2) financial aid measures, and (3) preparatory courses and programs. The findings show that there are little research and information about the actual outcomes of most measures to increase access to HE. We found a lack of adequate, reliable and consistently collected data about the policy instruments already put to practice. Since there is no excuse for the lack of effective action towards more equitable educational systems, more evidence-based approaches will be necessary to learn from these specific access measures and move forward towards more efficient equity policies.

Signal Detection Theory (SDT) is rarely used in higher education, yet has much potential in informing decision-making. In this methodological paper, we describe the potential of SDT for different higher education contexts and demonstrate its practical application. Both the commonly used regression analyses and SDT analyses provide information on the accuracy of a predictor, and thus which instrument(s) to use. SDT analyses, in addition, provide information on the effects of setting specific cut-off scores on outcomes of interest. SDT provides the sensitivity and specificity information for the chosen instrument(s) at specific cut-off scores (criteria in SDT). This allows for evidence-informed, deliberate choice of cut-off scores to steer toward desired outcomes. Depending on how undesirable false positives and false negatives are considered in a specific situation, a lower or higher cut-off score can be deemed adequate. Using SDT analyses in our example, we demonstrate how to use the results to optimize “real-life” student selection. However, selection is only one of many decision-making practices where SDT is applicable and valuable. We outline some of the areas within higher education decision-making and quality assurance, where SDT can be applied to answer specific questions and optimize decision-making.