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- Creators: Walker, Erin
- Creators: College of Integrative Sciences and Arts
- Member of: Theses and Dissertations
in the form of logs from students' tablets and the vocal interaction between pairs of students. Thousands of different features were defined, and then extracted computationally from the audio and log data. Human coders used richer data (several video streams) and a thorough understand of the tasks to code episodes as
collaborative, cooperative or asymmetric contribution. Machine learning was used to induce a detector, based on random forests, that outputs one of these three codes for an episode given only a characterization of the episode in terms of superficial features. An overall accuracy of 92.00% (kappa = 0.82) was obtained when
comparing the detector's codes to the humans' codes. However, due irregularities in running the study (e.g., the tablet software kept crashing), these results should be viewed as preliminary.
The study of developing a real-time cross-platform collaboration system between VR and MR takes into consideration a scenario in which multiple device users are connected to a multiplayer network where they are guided to perform various tasks concurrently.
Usability testing was conducted to evaluate participant perceptions of the system. Users were required to assemble a chair in alternating turns; thereafter users were required to fill a survey and give an audio interview. Results collected from the participants showed positive feedback towards using VR and MR for collaboration. However, there are several limitations with the current generation of devices that hinder mass adoption. Devices with better performance factors will lead to wider adoption.
In January of 2022, 61 women from Afghanistan were accepted to Arizona State University and joined our campus from the Asian University of Women. Of One Heart is a Phoenix based nonprofit which aims to connect refugees with mentors to cultivate intercultural relationships, assist refugees in integrating into a new community, and to empower refugees to utilize their unique perspectives and talents in their new home. In addition to these goals, these relationships aim to connect refugees with the networks their mentor has and to assist them in understanding the new systems and norms of American culture. The relationship is reciprocal in the sharing of background and stories to facilitate trust and to recognize the value refugees have to contribute to society. The mission of this project is to implement the Of One Heart mentoring model onto ASU campus to help facilitate intercultural friendships between our new students from Afghanistan and other ASU students, faculty and staff. In doing so, we hope to create a model demonstrating refugee student success by collecting data through pre and post program surveys to track if involvement in the program improved participants utilization of existing ASU resources, cultural competency, mental health, and participation in community activities and internships/job opportunities. Ideally, we hope to create a program model which is proven to support refugee students to be replicated for future semesters as the program expands to serve not only the students from Afghanistan, but all refugee and asylum seeking students.
Winners Circle is a collaborative application that allows friends, family members, and peers to communicate with each other about sports news and friendly wagers on teams and players. Through research and trial and error, a mock app was created by the team that combines breakout rooms that mimic a social media platform where users can identify news, scores, and perceptions of the outcome of games from other sports fans.
Transfer learning using different representations was also studied with a goal of building collaboration detectors for one task can be used with a new task. Data for building such detectors were collected in the form of verbal interaction and user action logs from students’ tablets. Three qualitative levels of interactivity were distinguished: Collaboration, Cooperation and Asymmetric Contribution. Machine learning was used to induce a classifier that can assign a code for every episode based on the set of features. The results indicate that machine learned classifiers were reliable and can transfer.