Within Psychology at the University of Glasgow, over the past few years, we have looked to refocus our research curriculum across all teaching levels to centre on reproducible methods, and to develop and improve academic communication practices consistent with the ideas of accountability and integrity in disseminating reproducible science. This change was partly based on the issues within our field, highlighted by the Replication Crisis, as well as on discussions and reflections about the necessary skills sought in a graduate researcher. In terms of our statistics and research method provision, the changes we have put in place mean our curriculum now promotes student independence in their research practice by emphasising traditionally overlooked skills, including, and in particular, data wrangling, visualisation, and dynamic reproducible report writing. In addition, students learn about concepts such as probability through simulation and how to work with data through "real and messy" open datasets, helping to build diverse practical skills and to develop their own competence and confidence in all areas of academic and data literacy. These developments have partly been made possible through a change of analytical software, and as such all our research methods classes are now taught in R, but fundamental to the approach has been the focus on the skills taught and not simply a change of software. Furthermore, we promote a focus on the importance of the research question and the hypothesis, as opposed to the outcome, by using assignments based on pre-registration and Registered Reports, as well as assessments discussing the benefits and limitations of open science practices, aiming to develop a more holistic view of the issues within our students and future graduates. Through honest discussions of different approaches and through developing a diverse skillset, we have looked to build a cohort that knows how research should be conducted, can discuss the reasoning behind those ideas, and has the skillset to implement that approach. That said, these developments have not been without issue and, throughout this talk, as well as highlighting what we have implemented and why, I will also discuss what has not worked and present advice on common implementation challenges, ranging from staff training to building an inclusive and supportive student community. In brief, I will share the rationale, experiences, and materials behind our approach to developing students as accountable practitioners of reproducible research with the hope that our experiences can be of benefit to a diverse range of fields. All our materials for teaching data wrangling and analysis are available at https://psyteachr.github.io/
Phil McAleer is a Lecturer in the School of Psychology at the University of Glasgow. One of his main areas of research looks at voice perception and how listener's extract and use information from a speaker's voice, such as emotion and personality. Phil is also very interested in how students learn and how they can build their skills through assessment and feedback. As one of the lecturers focussed on statistics and research methods in the school, Phil has spent the last few years developing content and approaches to help students engage and understand what for many is a challenging part of the field. Through the school's PsyTeachR materials he has helped build a curriculum focussed on data skills and on developing each student's own self confidence and competence in their ability. Phil is a Fellow of the Higher Education Academy, a co-lead of a local UK Reproducibility Network, and a Fellow of the Software Sustainability Initiative; a group aimed at enabling world-class research through better software.
Teaching Materials: https://psyteachr.github.io/