Northeastern University - Seattle
401 Terry Ave N
Seattle, WA 98109
ATTN: Abigail Evans, 401 Terry Ave N #103
360 Huntington Avenue
Boston, MA 02115-5000
- PhD, University of Washington
- MEd in Technology, Innovation, and Education, Harvard University
Abigail Evans is a lecturer at Northeastern University’s Khoury College of Computer Sciences in Seattle. She received her PhD from the University of Washington’s Information School in 2018, specializing in human computer interaction and computer supported collaborative learning. Evans also holds an MEd in Technology, Innovation, and Education from Harvard University.
Evans, A. C. 2019. Adaptive support for collaborative learning at tabletop computers. In Proceedings of the International Conference on Computer Supported Collaborative Learning (CSCL ’19). Lyon, France (June 17-20, 2019). ISLS.
Collaborative learning is a common practice in today’s classrooms. Technology-supported collaborative learning environments are becoming increasingly sophisticated, enabling new ways for students to work together with technology. Research has shown that collaborative learning has many benefits, particularly for developing students’ higher-order thinking and problemsolving skills. However, it has also been shown that students do not always know how to collaborate effectively, which can inhibit the success of collaborative learning. These findings suggest that collaboration itself is a skill that needs to be fostered and developed in the classroom.
Tabletop computers have affordances for collaborative learning because of the large, shared interface that multiple people can see and interact with at once. Despite these affordances, small group work at a tabletop computer is just as susceptible to breakdowns in collaboration as group work using other kinds of tools. Through design-based research in classroom settings, I have investigated how tabletop computers can model social regulation—the processes that groups use to manage and monitor their collective work—in order to detect when a group of students is in need of support. While collaboration is driven by the verbal and gestural interactions between the learners, the tabletop is only able to capture direct interaction with the device.
I have identified touch patterns that reflect the quality of social regulation and can be used to detect problems in the collaborative process. To enable the real-time use of these touch patterns, I developed a machine learning-based approach for distinguishing among simultaneous users at a tabletop computer. I also present software adaptations designed to encourage more effective collaboration that are triggered when breakdowns in collaboration are detected. A classroom evaluation of these adaptations showed that they deterred disruptive behavior and reduced the length of periods of sustained, low-quality collaboration.
My dissertation demonstrates the following thesis: Interactive tabletop software that can automatically detect breakdowns in collaboration and adapt in real-time to scaffold effective social regulation can improve secondary school students’ collaboration skills.
Group Touch: Distinguishing tabletop users in group settings via statistical modeling of touch pairs
Evans, A. C., Davis, K., Fogarty, J., Wobbrock, J. O. 2017. Group Touch: Distinguishing tabletop users in group settings via statistical modeling of touch pairs. In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI ’17). Denver, CO (May 6-11, 2017). ACM, New York, NY. 35-47.
We present Group Touch, a method for distinguishing among multiple users simultaneously interacting with a tabletop computer using only the touch information supplied by the device. Rather than tracking individual users for the duration of an activity, Group Touch distinguishes users from each other by modeling whether an interaction with the tabletop corresponds to either: (1) a new user, or (2) a change in users currently interacting with the tabletop. This reframing of the challenge as distinguishing users rather than tracking and identifying them allows Group Touch to support multi-user collaboration in real-world settings without custom instrumentation. Specifically, Group Touch examines pairs of touches and uses the difference in orientation, distance, and time between two touches to determine whether the same person performed both touches in the pair. Validated with field data from high-school students in a classroom setting, Group Touch distinguishes among users “in the wild” with a mean accuracy of 92.92% (SD=3.94%). Group Touch can imbue collaborative touch applications in real-world settings with the ability to distinguish among multiple users.
Evans, S. A., Davis, K., Evans, A. C., Campbell, J., Randall, D. P., Yin, K., Aragon, C. 2017. More than peer production: Fanfiction communities as sites of distributed mentoring. In Proceedings of the ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW ’17). Portland, OR (Feb 25-Mar 1, 2017). ACM, New York, NY. 259-272.
From Harry Potter to American Horror Story, fanfiction is extremely popular among young people. Sites such as Fanfiction.net host millions of stories, with thousands more posted each day. Enthusiasts are sharing their writing and reading stories written by others. Exactly how does a generation known more for videogame expertise than long-form writing become so engaged in reading and writing in these communities? Via a nine-month ethnographic investigation of fanfiction communities that included participant observation, interviews, a thematic analysis of 4,500 reader reviews and an in-depth case study of a discussion group, we found that members of fanfiction communities spontaneously mentor each other in open forums, and that this mentoring builds upon previous interactions in a way that is distinct from traditional forms of mentoring and made possible by the affordances of networked publics. This work extends and develops the theory of distributed mentoring. Our findings illustrate how distributed mentoring supports fanfiction authors as they work to develop their writing skills. We believe distributed mentoring holds potential for supporting learning in a variety of formal and informal learning environments.
Evans, A. C., Wobbrock, J. O., Davis, K. Modeling collaboration patterns on an interactive tabletop in a classroom setting. 2016. In Proceedings of the ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW ’16). San Francisco, CA (Feb 27-Mar 2, 2016). ACM, New York, NY. 860-871
Interaction logs generated by educational software can provide valuable insights into the collaborative learning process and identify opportunities for technology to provide adaptive assistance. Modeling collaborative learning processes at tabletop computers is challenging, as the computer is only able to log a portion of the collaboration, namely the touch events on the table. Our previous lab study with adults showed that patterns in a group’s touch interactions with a tabletop computer can reveal the quality of aspects of their collaborative process. We extend this understanding of the relationship between touch interactions and the collaborative process to adolescent learners in a field setting and demonstrate that the touch patterns reflect the quality of collaboration more broadly than previously thought, with accuracies up to 84.2%. We also present an approach to using the touch patterns to model the quality of collaboration in real-time.
Campbell, J., Aragon, C., Davis, K., Evans, S. A., Evans, A. C., Randall, D. P. 2016. Thousands of Positive Reviews: Distributed mentoring in online fan communities. In Proceedings of the ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW ’16). San Francisco, CA (Feb 27-Mar 2, 2016). ACM, New York, NY. 691-704.
Young people worldwide are participating in ever-increasing numbers in online fan communities. Far from mere shallow repositories of pop culture, these sites are accumulating significant evidence that sophisticated informal learning is taking place online in novel and unexpected ways. In order to understand and analyze in more detail how learning might be occurring, we conducted an in-depth nine-month ethnographic investigation of online fanfiction communities, including participant observation and fanfiction author interviews. Our observations led to the development of a theory we term distributed mentoring, which we present in detail in this paper. Distributed mentoring exemplifies one instance of how networked technology affords new extensions of behaviors that were previously bounded by time and space. Distributed mentoring holds potential for application beyond the spontaneous mentoring observed in this investigation and may help students receive diverse, thoughtful feedback in formal learning environments as well.
Evans, A. C., Wobbrock, J. O. 2014. Filling in the gaps: Capturing social regulation in an interactive tabletop learning environment. In Proceedings of the International Conference of the Learning Sciences (ICLS ’14). Boulder, CO (June 23-27). International Society of the Learning Sciences. 1157-1161.
A study of small groups collaborating at an interactive tabletop was conducted. Group discussions were coded according to the type and quality of social regulation processes used. Episodes of high and low quality social regulation were then matched with the software logs to identify patterns of interaction associated with quality of social regulation. A key finding is that instances of low-quality social regulation were characterized by more than twice as much interaction with the software as high-quality instances.
Taming wild behavior: The Input Observer for obtaining text entry and mouse pointing measures from everyday computer use
Evans, A. C., Wobbrock, J. O. 2012. Taming wild behavior: The Input Observer for obtaining text entry and mouse pointing measures from everyday computer use. In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI ’12). Austin, TX (May 5-10, 2012). ACM, New York, NY. 1947-1956.
We present the Input Observer, a tool that can run quietly in the background of users’ computers and measure their text entry and mouse pointing performance from everyday use. In lab studies, participants are presented with prescribed tasks, enabling easy identification of speeds and errors. In everyday use, no such prescriptions exist. We devised novel algorithms to segment text entry and mouse pointing input streams into “trials”. We are the first to measure errors for unprescribed text entry and mouse pointing. To measure errors, we utilize web search engines, adaptive offline dictionaries, an Automation API, and crowdsourcing. Capturing errors allows us to employ Crossman’s (1957) speed-accuracy normalization when calculating Fitts’ law throughputs. To validate the Input Observer, we compared its measures from 12 participants over a week of computer use to the same participants’ results from a lab study. Overall, in the lab and field, average text entry speeds were 74.47 WPM and 80.59 WPM, respectively. Average uncorrected error rates were near zero, at 0.12% and 0.28%. For mouse pointing, average movement times were 971 ms and 870 ms. Average pointing error rates were 4.42% and 4.66%. Average throughputs were 3.48 bits/s and 3.45 bits/s. Device makers, researchers, and assistive technology specialists may benefit from measures of everyday use.