Action-based Skills Development: Implications of Spoken Language Data Sessions

Yanyan Li, Dr. Lyu Zhang

2024

Summary

Studies on doctoral research skills training (e.g. Strayhorn, 2016; Stevanovic & Weiste, 2017) have reported an imbalance between learning about and learning to do research. Specifically, research students are instructed to make methodological choices based on theory-driven research questions, relying on experiential and ‘common sense’ evidence for methodological decision making (Perry & Smart, 2007). Such theory-driven training is limited, mainly discussing theoretical approaches with little support to students in working with actual data. Learning a research practice requires more than theoretical descriptions, and face-to-face interactions with more experienced researchers need to be built into research skills development (Stevanovic & Weiste, 2017).

To address this issue requires integrating theory-based and action-based components in doctoral research skills training, aligning with the evidence based pedagogical shift in UK higher education. Action-based refers to taking analytic actions in response to the situated practices in the data. One effective approach to address action-based elements for researchers working with spoken language data (SLD) and facilitate novice-expert conversations is incorporating data sessions. A data session involves researchers’ (e.g. professors, PhDs) collaborative analysis of SLD. In SLD sessions, participants share contextual information of their SLD, scrutinize the transcripts, and showcase analytic observations followed by free discussions about the data. Warwick Interaction & Talk (WIT) Network organises SLD sessions weekly. In this project, besides regular SLD sessions, we plan to invite internal and external scholars to interactive workshops for panel discussions about working with SLD.

Building on WIT’s existing work, this project aims to investigate the implications of SLD sessions for doctoral research skills development from evidence-based and action-based perspectives. It also aims to explore participants’ interactional dynamics in SLD sessions and their socialisation into research praxes (e.g. equality and mutuality in conversations involving researchers at all levels). Finally, we aim to generate tangible resources for enhancing research skills and provide guidelines on working with SLD. We will address three questions: 1) How can SLD sessions enhance doctoral researchers’ skills in working with SLD analysis? 2) How are participants socialised into the research praxis with SLD and establish social connections in SLD sessions? 3) What resources can the researchers acquire from attending SLD sessions? We will collect and analyse data of a) video/audio recordings of SLD sessions held in terms 2&3; b) audio recordings of panel discussions on working with SLD; c) audio recordings of individual interviews with participants reflecting on the sessions. Thematic analysis will be conducted to analyse datasets (b) and (c), answering RQ1 and RQ3. Dataset (a) will be analysed using conversation-analytic methods to examine how the participants co-construct understanding and build relationships, addressing RQ2. Recorded SLD sessions will be used in the interview for stimulated recall to solicit participants’ reflection.

The findings can offer constructive advice to Warwick on developing practice oriented doctoral research skills training to equip doctoral researchers with effective research skills transferable to their own research, and present the effective skillset developed in working with SLD. Since SLD is widely used across disciplines, this project will help encourage interdisciplinary engagement among students and academic staff, developing a community of researchers working with SLD.

Assets