Distributed mentoring and fanfiction data analytics
Co-directed by Cecilia Aragon and John Frens
This ongoing research project studies informal learning in online fanfiction communities. We are looking for students with experience in either (a) programming and analysis of large text datasets or (b) machine learning and data science, to join an existing research group.
We’ve collected a vast, rich text dataset of over 61.5 billion words (the largest fiction dataset outside of the Google Books corpus) of stories, reviews, and associated metadata from fanfiction sites and have applied both qualitative (ethnography) and quantitative techniques (machine learning, statistical analysis, data visualization) to investigate the relationship between distributed mentoring and writing quality (e.g., grammar, reading level). We have published multiple papers on our research and are in the process of submitting others.
We have found quantitative evidence that distributed mentoring plays a positive role in fanfiction authors’ development as writers, and this quarter’s project continues our efforts with a specific focus on quantitative analysis of our large dataset.
Interested undergraduate and graduate students may apply. Applicants should send an email to both Cecilia Aragon <firstname.lastname@example.org> and John Frens <email@example.com>, including a short paragraph describing your interest and qualifications for the group, a resume, and an unofficial transcript.
We are looking for a relatively small group of people who are each interested in between 2 and 5 credit hours of credit/no credit grade in HCDE 496/596.
We will meet Wednesdays, 6-7:30 p.m., in Sieg 129 during spring quarter.