Institute of Education

The 50th volume in the book series ‘Global Perspectives on Higher Education' offers a stimulating and thoughtful assessment of higher education from a global perspective which addresses the challenges and prospects for the next decade. The challenges now faced by higher education and its likely future prospects and patterns are examined in terms of policy papers and case studies. Five broad topics are considered: the situation of academic faculty, the demand for access, the role of the university in society and its governance, funding trends, and higher education’s international dimensions.

This research investigates how learning groups affect student learning from two perspectives: first, the amount of group work students do, and second, the role that they take within the group. It is not clear from the current research how a student’s role in collaborative learning affects his/her development of critical thinking and the construction of knowledge. The present study looks into whether the positive relationships found between collaboration and germane cognitive load are affected by a learner’s role within the group. Using cognitive load theory, this study analyzed survey responses from a group of university students (n = 1399) who engaged in collaborative study groups when taking online classes in South Korea. While it was found that the amount of collaboration a student engaged in positively affected levels of germane load and that their level of contribution negatively moderated that relationship. In other words, while more group work is beneficial, students who contribute less to the group have greater gains from higher levels of collaboration than students who take a more active role.

Many important decisions in societies such as school admissions, hiring, or elections are based on the selection of top-ranking individuals from a larger pool of candidates. This process is often subject to biases, which typically manifest as an under-representation of certain groups among the selected or accepted individuals. The most common approach to this issue is debiasing, for example via the introduction of quotas that ensure proportional representation of groups with respect to a certain, often binary attribute. Cases include quotas for women on corporate boards or ethnic quotas in elections. This, however, has the potential to induce changes in representation with respect to other attributes. For the case of two correlated binary attributes we show that quota-based debiasing based on a single attribute can worsen the representation of already underrepresented groups and decrease overall fairness of selection. We use several data sets from a broad range of domains from recidivism risk assessments to scientific citations to assess this effect in real-world settings. Our results demonstrate the importance of including all relevant attributes in debiasing procedures and that more efforts need to be put into eliminating the root causes of inequalities as purely numerical solutions such as quota-based debiasing might lead to unintended consequences.