PUTTING THE PASS IN CLASS: IN-CLASS PEER MENTORING ON CAMPUS AND ONLINE

We analyse the introduction of peer mentors into classrooms to understand how in-class mentoring supports students’ learning in first-year courses. Peer mentors are high-achieving students who have completed the same course previously, and are hired and trained by the university to facilitate Peer Assisted Study Sessions (PASS). PASS sessions give students the opportunity to deepen their understanding through revision and active learning and are typically held outside of class time. In contrast, our trial embedded peer mentors into the classes for Professional Scientific Thinking, a large (~250 students) workshop-based course at the University of Newcastle. Analysis of Blackboard analytics, student responses to Brookfield’s Critical Incident Questionnaire and peer mentors’ journals found that during face-to-face workshops, peer mentors role-modelled ideal student behaviour (e.g. asking questions), rather than act as additional teachers. This helped students new to university to better understand how to interact and learn effectively in class. Moving classes online mid-semester reshaped mentors’ roles, including through the technical aspects of their work and their engagement with students – adaptations that were essential for supporting students to also adapt effectively to changed learning circumstances. This study highlights the benefits of embedding student mentors in classrooms, both on campus and online

Putting the PASS in Class: Peer Mentors’ Identities in Science Workshops on Campus and Online

In this paper, we analyse the introduction of peer mentors into timetabled classes to understand how in-class mentoring supports students’ learning. The peer mentors in this study are high-achieving students who previously completed the same course and who were hired and trained to facilitate Peer Assisted Study Sessions (PASS). PASS gives students the opportunity to deepen their understanding through revision and active learning and are typically held outside of class time. In contrast, our trial embedded peer mentors into classes for a large (~250 students) first-year workshop-based course. We employed a participatory action research methodology to facilitate the peer mentors’ co-creation of the research process. Data sources include peer mentors’ journal entries, student cohort data, and a focus group with teaching staff. We found that during face-to-face workshops, peer mentors role-modelled ideal student behaviour (e.g., asking questions) rather than acting as additional teachers, and this helped students to better understand how to interact effectively in class. The identity of embedded peer mentors is neither that of teachers nor of students, and it instead spans aspects of both as described using a three-part schema comprising (i) identity, (ii) associated roles, and (iii) associated practices. As we moved classes online mid-semester in response to the COVID-19 pandemic, mentors’ identities remained stable, but mentors adjusted their associated roles and practices, including through the technical aspects of their engagement with students. This study highlights the benefits of embedding mentors in classrooms on campus and online

Ethical, legal and social issues in diversifying genomic data: literature review and synthesis

Advances in technology have resulted in the ability to sequence entire human genomes as a routine, relatively inexpensive, investigation in healthcare. This offers many promises of personalising, stratifying, and targeting healthcare with an understanding of genetic susceptibility to particular diseases or conditions. However, research collections (databases, biobanks etc) that underpin these developments are significantly skewed towards populations of European ancestry meaning that our understanding of genetic susceptibility (or indeed of genetic protection to disease) is less good for many other populations in the world. Just as a dermatology text book skewed towards skin problems on white skin may be less useful to black populations, so genomic knowledge derived from one particular ancestry means it may be less useful to people with different ancestries. The need to diversify genomic data, to improve the evidence base for genomic medicine for all ancestries, is well recognised, but is more complex than simply increasing the collection of data from people from a range of ancestries. We reviewed the literature to understand the challenges of diversifying genomic data to identify key ethical, legal and social issues. Our findings were: 1. Many research practices are exclusionary and need to change. Examples include approaches to recruitment or data collection that do not consider the cultural setting in which potential participants are situated. Research also often lacks reflexivity about diversity on the part of researchers and research institutions. 2. Co-design is key to identifying and avoiding potential problems around data diversification. This requires an understanding of the concerns of underserved individuals and communities regarding exploitation and stigmatisation, as well as issues of data ownership and sovereignty. Without attention to group as well as individual concerns, participant engagement may become tokenistic which in turn risks exacerbating existing, as well as creating new, inequalities. 3. There are wider structural issues that influence researchers’ and participants’ attempts to generate diverse data. For example, (a) some researchers view data as neutral, but this ignores the social construction of data and technologies, and their tendencies to reflect societal inequalities. (b). Efforts to diversify data should be contextualised within the historical trajectory of structural racism and legacies of colonialism. (c) Classification and categorisation of populations have political consequences and need to be closely interrogated. These findings show that deliberation between researchers and participants, during all stages of research from planning and recruitment through to analysis, interpretation and dissemination is key to successful diversification

Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows

We introduce atomate, an open-source Python framework for computational materials science simulation, analysis, and design with an emphasis on automation and extensibility. Built on top of open source Python packages already in use by the materials community such as pymatgen, FireWorks, and custodian, atomate provides well-tested workflow templates to compute various materials properties such as electronic bandstructure, elastic properties, and piezoelectric, dielectric, and ferroelectric properties. Atomate also enables the computational characterization of materials by providing workflows that calculate X-ray absorption (XAS), Electron energy loss (EELS) and Raman spectra. One of the major features of atomate is that it provides both fully functional workflows as well as reusable components that enable one to compose complex materials science workflows that use a diverse set of computational tools. Additionally, atomate creates output databases that organize the results from individual calculations and contains a builder framework that creates summary reports for each computed material based on multiple simulations