No abstract

Purpose This paper presents the results of a qualitative study that involved students of an interdisciplinary PhD program. The study objective was to gather requirements to create a knowledge graph information system. The purpose of this study was to determine information-seeking practices and information needs of this community, to inform the functionalities of a proposed system, intended to help students with relevant resource discovery and decision-making. Design/methodology/approach The study design included semi-structured interviews with eight members of the community, followed by a website usability study with the same student participants. Findings Two main information-seeking styles are recognized and reported through user personas of international and domestic (USA) students. The findings show that the useful information resides within the community and not so much on the program website. Students rely on peer communication, although they report lack of opportunities to connect. Students’ information needs and information seeking are dependent on their progress through the program, as well as their motivation and the projected timeline. Practical implications Considering the current information needs and practices, a knowledge graph hosting both information on social networks and the knowledge produced by the activities of the community members would be useful. By recording data on their activities (for example, collaboration with professors and coursework), students would reveal further useful system functionalities and facilitate transfer of tacit knowledge. Originality/value Aside from the practical value of this research that is directly influencing the design of a system, it contributes to the body of knowledge on interdisciplinary PhD programs.

How do PhD students discover the resources and relationships conducive to satisfaction and success in their degree programs? This study proposes a community-grounded, extensible knowledge graph to make explicit and tacit information intuitively discoverable, by capturing and visualizing relationships between people based on their activities and relations to information resources in a particular domain. Students in an interdisciplinary PhD program were engaged through three workshops to provide insights into the dynamics of interactions with others and relevant data categories to be included in the graph data model. Based on these insights we propose a model, serving as a testbed for exploring multiplex graph visualizations and a potential basis of the information system to facilitate information discovery and decision-making. We discovered that some of the tacit knowledge can be explicitly encoded, while the rest of it must stay within the community. The graph-based visualization of the social and knowledge networks can serve as a pointer toward the people having the relevant information, one can reach out to, online or in person.