Fankhauser, S. C. | Search bioRxiv

bioRxiv2024

Empowering future scientists: Exploring How Mentors Guide Students to Write and Publish Scientific Papers

Peer-review and publication are important parts of the scientific enterprise, and research has shown that engaging students in such scholarly practices helps build their sense of belonging and scientific identity. Yet, these disciplinary literacy skills and professional practices are often part of the hidden curriculum of science research, thus excluding students and others from fully understanding ways in which scientific knowledge is constructed, refined, and disseminated even though students are participating in such activities. Secondary students are increasingly involved in scientific research projects that include authentic disciplinary literacy components such as research proposals, posters, videos, and scientific research papers. More and more, students are also engaging in professional practice of publishing their scientific research papers through dedicated secondary science journals. How teachers and other mentors support the development of professional disciplinary literacies in students is critical to understand as part of supporting more student participation in research. To this end, we used a mixed- methods study of interviews and surveys to examine the experience and conceptions of the mentors (teachers and professional scientists) who guided pre-college students through the writing and publication of their scientific research projects. Analyzing our data from a lens of cognitive apprenticeship, we find that mentors encourage independence by primarily employing the method of "exploration". We also find that mentors have divergent views on the value of publication within science, versus for student scientists specifically. Our findings suggest that mentors could work to explicitly reveal their own thinking within science writing to provide more sequenced support for student scientists.

scientific communication and education↗

bioRxiv2023

Use of Large Language Models to Aid Analysis of Textual Data

The increasing use of machine learning and Large Language Models (LLMs) opens up opportunities to use these artificially intelligent algorithms in novel ways. This article proposes a methodology using LLMs to support traditional deductive coding in qualitative research. We began our analysis with three different sample texts taken from existing interviews. Next, we created a codebook and inputted the sample text and codebook into an LLM. We asked the LLM to determine if the codes were present in a sample text provided and requested evidence to support the coding. The sample texts were inputted 160 times to record changes between iterations of the LLM response. Each iteration was analogous to a new coder deductively analyzing the text with the codebook information. In our results, we present the outputs for these recursive analyses, along with a comparison of the LLM coding to evaluations made by human coders using traditional coding methods. We argue that LLM analysis can aid qualitative researchers by deductively coding transcripts, providing a systematic and reliable platform for code identification, and offering a means of avoiding analysis misalignment. Implications of using LLM in research praxis are discussed, along with current limitations.

scientific communication and education↗