@article {9999, title = {Building an apparatus: Refractive, reflective and diffractive readings of trace data}, journal = {Journal of the Association for Information Systems}, volume = {21}, year = {2020}, pages = {Article 10}, abstract = {

We propose a set of methodological principles and strategies for the use of trace data, i.e., data capturing performances carried out on or via information systems, often at a fine level of detail. Trace data comes with a number of methodological and theoretical challenges associated with the inseparable nature of the social and material. Drawing on Haraway and Barad{\textquoteright}s distinctions among refraction, reflection and diffraction, we compare three approaches to trace data analysis. We argue that a diffractive methodology allows us to explore how trace data are not given but created though construction of a research apparatus to study trace data. By focusing on the diffractive ways in which traces ripple through an apparatus, it is possible to explore some of the taken-for-granted, invisible dynamics of sociomateriality. Equally, important this approach allows us to describe what and when distinctions within entwined phenomena emerge in the research process. Empirically, we illustrate the guiding principles and strategies by analyzing trace data from Gravity Spy, a crowdsourced citizen science project on Zooniverse. We conclude by suggesting that a diffractive methodology may help us draw together quantitative and qualitative research practices in new and productive ways that also raises interesting design questions.

}, doi = {10.17705/1jais.00590 }, attachments = {https://citsci.syr.edu/sites/crowston.syr.edu/files/RA-JAIS-17-0130.R3.1_FIN\%20to\%20share.pdf}, author = {Carsten {\O}sterlund and Kevin Crowston and Corey Brian Jackson} } @proceedings {9999, title = {Blending machine and human learning processes}, year = {2017}, abstract = {

Citizen science projects rely on contributions from volunteers to achieve their scientific goals and so face a dilemma: providing volunteers with explicit training might increase the quality of contributions, but at the cost of losing the work done by newcomers during the training period, which for many is the only work they will contribute to the project. Based on research in cognitive science on how humans learn to classify images, we have designed an approach to use machine learning to guide the presentation of tasks to newcomers that help them more quickly learn how to do the image classification task while still contributing to the work of the project. A Bayesian model for tracking this learning is presented.

}, doi = {10.24251/HICSS.2017.009}, url = {http://hdl.handle.net/10125/41159}, attachments = {https://citsci.syr.edu/sites/crowston.syr.edu/files/training\%20v3\%20to\%20share_0.pdf}, author = {Kevin Crowston and Carsten {\O}sterlund and Lee, Tae Kyoung} } @proceedings {2015, title = {Being Present in Online Communities: Learning in Citizen Science}, year = {2015}, address = {Limerick, Ireland}, abstract = {

How online community members learn to become valuable contributors constitutes a long-standing concern of Community \& Technology researchers. The literature tends to highlight participants{\textquoteright} access to practice, feedback from experienced members, and relationship building. However, not all crowdsourcing environments offer participants opportunities for access, feedback, and relationship building (e.g., Citizen Science). We study how volunteers learn to participate in a citizen science project, Planet Hunters, through participant observation, interviews, and trace ethnography. Drawing on S{\o}rensen{\textquoteright}s sociomaterial theories of presence, we extend the notion of situated learning to include several modes of learning. The empirical findings suggest that volunteers in citizen science engage more than one form of access to practice, feedback, and relationship building. Communal relations characterize only one form of learning. Equally important to their learning are authority{\textendash}subject and agent-centered forms of access, feedback, and relationship building.

}, doi = {10.1145/2768545.2768555}, attachments = {https://citsci.syr.edu/sites/crowston.syr.edu/files/C\%26T_2015_FINAL.pdf}, author = {Gabriel Mugar and Carsten {\O}sterlund and Corey Jackson and Kevin Crowston} }