Publication Date
2022
Document Type
Dissertation
Committee Members
Yong Pei, Ph.D. (Committee Chair); Nia S. Peters, Ph.D. (Committee Co-Chair); Shengrong Cai, Ph.D. (Committee Member); Krishnaprasad Thirunarayan, Ph.D. (Committee Member); Paul J. Hershberger, Ph.D. (Committee Member); Mateen M. Rizki, Ph.D. (Committee Member)
Degree Name
Doctor of Philosophy (PhD)
Abstract
Task success in co-located and distributed collaborative work settings is characterized by clear and efficient communication between participating members. Communication issues like 1) Unwanted interruptions and 2) Delayed feedback in collaborative work based distributed scenarios have the potential to impede task coordination and significantly decrease the probability of accomplishing task objective. Research shows that 1) Interrupting tasks at random moments can cause users to take up to 30% longer to resume tasks, commit up to twice the errors, and experience up to twice the negative effect than when interrupted at boundaries 2) Skill retention in collaborative learning tasks improves with immediate feedback dissemination. To address the negative impact of these communication issues, this dissertation presents two multi-user, multi-tasking collaborative work scenarios and illustrates respective real-time fully functional computer supported cooperative work (CSCW) based prototypes. ACE-IMS leverages lexical affirmation cues which are indicative of task boundaries to intelligently identify “the right time to interrupt” and ReadMI assesses Motivational Interviewing (MI) based clinician-client dialogue in collaborative learning environment to identify speaker intents like open-ended questions, close-ended questions, reflective statements and scale enquiring statements and provide quantitative feedback to assist the facilitator in comprehensive practitioner skill assessment. To implement these functionalities both systems leverage task-oriented dialogues as datasets and utilize natural language processing with latest developments in ubiquitous technologies like mobile-cloud computing, computational linguistics, and deep learning. This research goes a step further in demonstrating the usability of CSCW based system designs by reporting qualitative and quantitative user feedback data by deploying ReadMI in an actual collaborative learning environment. The participants agree that ReadMI based metrics provide a tangible way to measure practitioner progress and offsets facilitator workload, showing a strong potential to enhance collaborative work experience.
Page Count
136
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
2022
Copyright
Copyright 2022, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.
ORCID ID
0000-0003-0582-014X
