Patrick Buckley

Higher education institutes around the world are adjusting to a rapidly changing environment caused by macro level trends such as massification and marketization. As part of this process, higher education institutions are seeking to enhance the student experience and build deeper, mutually beneficial relationships with their alumni. This involves encouraging loyalty in students and alumni from the outset of the relationship. This paper studies the impact of extra-curricular activities on students’ loyalty to their educational institution. A quantitative survey was conducted in an Irish HEI ( n = 798) and a French HEI ( n = 670). The data collected suggests that participation in extra-curricular activities has a significantly positive effect on student’s loyalty to their institution which we posit will enhance their loyalty as eventual alumni. Institutional loyalty is culturally sensitive and needs to be managed as such, but the effect of participation in extra-curricular activities on loyalty is consistent across cultural contexts. These findings suggest that institutions should support extra-curricular activities as an investment in building loyalty in students and alumni.

The research ethics review systems within universities evolved from the positivist biomedical model but have expanded to include all non-clinical research involving human subjects. However, the application of the biomedical paradigm to qualitative research often creates significant problems. This article highlights the fundamental differences between biomedical and humanities and social science (HSS) research, illustrating that one size does not fit all when it comes to research ethics review. Recognising the resource constraints faced by many higher level education institutions, we develop a model which encompasses the traditional research ethics concepts without requiring separate oversight procedures. After its original construction based on extent research ethics literature, the model was evolved based on findings from qualitative interviews carried out with expert members of research ethics committees. The model can be adapted to multiple contexts through the application of different levels of tolerance in each domain. Our contribution is twofold: (1) to synthesise from the literature an explicit rationale for differentiating research contexts when it comes to research ethics oversight; and (2) to provide research ethics committees with a workable visual model that can be used to aid decision making in diverse research domains.

This paper outlines how an existing collaborative forecasting tool called a prediction market (PM) can be integrated into an educational context to enhance information literacy skills and cognitive disciplinary knowledge. The paper makes a number of original contributions. First, it describes how this tool can be packaged as a pedagogical intervention which can be used in a range of contexts across multiple disciplines. This is illustrated by a case study, which describes the addition of a PM assignment to an undergraduate module. Second, using both quantitative and qualitative data, the paper empirically investigates the impact of PM participation on engagement in information search and cognitive disciplinary knowledge. Our results show that participant's behaviors are altered by the intervention, and that students actively search for information related to the forecasting problems presented. We also demonstrate that students' disciplinary knowledge is enhanced by the intervention.

Mass higher education presents serious problems to implementing active learning. Large class sizes means that traditional active learning strategies are becoming more and more difficult to implement, due to the administrative burdens associated with them. In this chapter, the authors present prediction markets as a pedagogical tool which can be used to allow teachers to implement active learning in a large group teaching environment without imposing prohibitive administrative overheads. They outline the benefits for students in the cognitive and affective domains of learning. They move on to present a case study describing in detail how our methodology can be implemented and conclude by presenting research on the effectiveness of our approach in the cognitive domain of learning. The authors conclude that prediction markets are a powerful tool for implementing active learning in a large group teaching environment.