Student-led research workshops for improving “sense of belonging” in BME students in PhD programmes
The programme will recruit PhD students and undergraduate students that identify as BME. We envisage our project will benefit both cohorts, acting as a encouragement to increasing the number of students that identify as BME in graduate programmes (by fostering their relationship with current PhD students and their understanding of what research may look like), as well as retaining the current graduate students (by giving them a platform to share their research and a financial contribution for their work).
We plan to engage with both PhD and UG students in the Department of Informatics that identify as Black or Minority Ethnic (BME). In the department, the proportion of overall students that are home students and that identify as BME shrinks from 36% at undergraduate level to 19% at postgraduate taught level, and to only 11% at PhD level.
The under-representation of BME students at the graduate school level is potentially a discouraging factor for our UG students to continue onto graduate level programmes themselves; for UG students, lack of role-models from similar backgrounds can negatively impact their sense of belonging, and likelihood to pursue graduate studies themselves. There are also limited opportunities for graduate students to present their research in more holistic settings, and to get experience with supervision; this is more of a challenge for those from BME backgrounds, who may feel less comfortable with the existing, more traditional opportunities to do so.
PhD students will be paired up with small groups of undergraduate students (e.g. 2 or 3). The matching will be done based on the relationship between the graduate student's research and a module UGs are taking. For example, a PhD student working with “Incentive-aware digital twins for finance” could match with the “Multi-Agent Systems” undergraduate course. Also, a student working with AI for hospital data may want to match with an introductory or advanced AI undergraduate course.
For each group, there will be then a series of four mini-workshops, in which the PhD student presents their research in relation to the content of the relevant UG course, and fosters discussions with the UG participants. The exact structure of these sessions will be holistic, and ultimately down to the PhD student to develop; we want to avoid imposing a formal setting that may be less comfortable for the students, and allow them to instead grow a natural and relaxed environment for supportive discussion.
Towards the end of the academic year, once all mentor sessions have been run, we plan to run a “showcase of ideas”; each UG student participant will be invited to present a short, 3 minute presentation on a research question that has come out of conversations with their mentor, which they could feasibly pursue as part of a PhD programme. This gives the UG participants the opportunity to share with each other their experiences, but also gives the platform for them to identify as researchers themselves in a friendly and supportive environment, thus reinforcing the idea that a PhD programme could be for them.