Expectation,motivation, engagement and ownership: using student reflections in the conative and affective domains to enhance residential field courses

Residential field courses are important and should be designed and delivered to maximize their value to students, staff and institutions. In this context, we use a novel approach involving analysis of the daily affective and conative reflections of students immersed in the field course experience to better understand student engagement with fieldwork. We show that students base their field course choice on a range of factors (costs and benefits) and that these choices subsequently influence student expectations and motivation to engage with fieldwork. We also show that the motivation of students to engage with fieldwork-based learning varies from person to person and from day to day. Our findings suggest that having a more nuanced understanding of the decisions students make when deciding which field course to enrol upon would enhance our ability to design attractive, accessible and useful field courses; that having an awareness of the expectations of students around field courses would enable us to better prepare them to undertake them; and that students are more motivated when they are afforded an opportunity to work independently and perceive themselves to have ownership of their learning.     

SPID: A New Database for Inferring Public Policy Innovativeness and Diffusion Networks

Despite its rich tradition, there are key limitations to researchers' ability to make generalizable inferences about state policy innovation and diffusion. This paper introduces new data and methods to move from empirical analyses of single policies to the analysis of comprehensive populations of policies and rigorously inferred diffusion networks. We have gathered policy adoption data appropriate for estimating policy innovativeness and tracing diffusion ties in a targeted manner (e.g., by policy domain, time period, or policy type) and extended the development of methods necessary to accurately and efficiently infer those ties. Our state policy innovation and diffusion (SPID) database includes 728 different policies coded by topic area. We provide an overview of this new dataset and illustrate two key uses: (i) static and dynamic innovativeness measures and (ii) latent diffusion networks that capture common pathways of diffusion between states across policies. The scope of the data allows us to compare patterns in both across policy topic areas. We conclude that these new resources will enable researchers to empirically investigate classes of questions that were difficult or impossible to study previously, but whose roots go back to the origins of the political science policy innovation and diffusion literature.