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You searched for +publisher:"University of North Carolina" +contributor:("Stotts, P. David"). Showing records 1 – 3 of 3 total matches.

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University of North Carolina

1. Carter, Jason. Automatic Difficulty Detection.

Degree: Computer Science, 2014, University of North Carolina

Previous work has suggested that the productivity of developers increases when they help each other and as distance increases, help is offered less. One way to make the amount of help independent of distance is to develop a system that automatically determines and communicates developers' difficulty. It is our thesis that automatic difficulty detection is possible and useful. To provide evidence to support this thesis, we developed six novel components: * programming-activity difficulty-detection * multimodal difficulty-detection * integrated workspace-difficulty awareness * difficulty-level detection * barrier detection * reusable difficulty-detection framework Programming-activity difficulty-detection mines developers' interactions. It is based on the insight that when developers are having difficulty their edit ratio decreases while other ratios such as the debug and navigation ratios increase. This component has a low false positive rate but a high false negative rate. The high false negative rate limitation is addressed by multimodal difficulty-detection. This component mines both programmers' interactions and Kinect camera data. It is based on the insight that when developers are having difficulty, both edit ratios and postures often change. Integrated workspace-difficulty awareness combines continuous knowledge of remote users' workspace with continuous knowledge of when developers are having difficulty. Two variations of this component are possible based on whether potential helpers can replay developers' screen recordings. One limitation of this component is that sometimes, potential helpers spend a large amount of time trying to determine if they can offer help. Difficulty-level and barrier detection address this limitation. The former is based on the insight that when developers are having surmountable difficulties they tend to perform a cycle of editing and debugging their code; and when they are having insurmountable difficulties they tend to spend a large amount of time a) between actions and b) outside of the programming environment. Barrier detection infers two kinds of difficulties: incorrect output and design. This component is based the insight that when developers have incorrect output, their debug ratios increase; and when they have difficulty designing algorithms, they spend a large amount of time outside of the programming environment. The reusable difficulty-detection framework uses standard design patterns to enable programming-activity difficulty-detection to be used in two programming environments, Eclipse and Visual Studio. These components have been validated using lab and/or field studies. Advisors/Committee Members: Carter, Jason, Dewan, Prasun, Brooks, Edward F., Kelly, Diane, Stotts, P. David, Wang, Wei.

Subjects/Keywords: Computer science; College of Arts and Sciences; Department of Computer Science

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Carter, J. (2014). Automatic Difficulty Detection. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:a7dcfe9b-f509-4715-85a0-e167b4976c25

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Carter, Jason. “Automatic Difficulty Detection.” 2014. Thesis, University of North Carolina. Accessed April 13, 2021. https://cdr.lib.unc.edu/record/uuid:a7dcfe9b-f509-4715-85a0-e167b4976c25.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Carter, Jason. “Automatic Difficulty Detection.” 2014. Web. 13 Apr 2021.

Vancouver:

Carter J. Automatic Difficulty Detection. [Internet] [Thesis]. University of North Carolina; 2014. [cited 2021 Apr 13]. Available from: https://cdr.lib.unc.edu/record/uuid:a7dcfe9b-f509-4715-85a0-e167b4976c25.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Carter J. Automatic Difficulty Detection. [Thesis]. University of North Carolina; 2014. Available from: https://cdr.lib.unc.edu/record/uuid:a7dcfe9b-f509-4715-85a0-e167b4976c25

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of North Carolina

2. Gyllstrom, Karl. Enriching personal information management with document interaction histories.

Degree: Computer Science, 2009, University of North Carolina

Personal information management is increasingly challenging, as more and more of our personal and professional activity migrates to personal computers. Manual organization and search remain the only two options available to users, and both have significant limitations; the former requires too much effort on the part of the user, while the latter is dependent on users' ability to recall discriminating information. I pursue an alternative approach, where users' computer interactions with their workspaces are recorded, algorithms draw inferences from this interaction, and these inferences are applied to improve information management and retrieval for users. This approach requires no effort from users and enables retrieval to be more personalized, natural, and intuitive. The Passages system enhances information management by maintaining a detailed chronicle of all the text the user ever reads or edits, and making this chronicle available for rich temporal queries about the user's information workspace. Passages enables queries like, which papers and web pages did I read when writing the related work section of this paper?, and which of the emails in this folder have I skimmed, but not yet read in detail? As time and interaction history are important attributes in users' recall of their personal information, effectively supporting them creates useful possibilities for information retrieval. I present methods to collect information about the large volume of text with which the user interacts, and use this information to improve retrieval. I show through user evaluation the accuracy of Passages in building interaction history, and illustrate its capacity to both improve existing retrieval systems and enable novel ways to characterize document activity across time. Before the Passages system, I developed two other systems with similar goals. Confluence extends an existing system that identifies task-based links among users' data through their being used at proximal points in time. For example, if a user frequently interacts with a report and a graph at the same time, those documents likely share a common task even though they may have no semantic relationship. Once such links are identified, they are applied when users issue search queries, expanding traditional, text-based results with other documents that share task-based links to those results. This creates a form of task-based retrieval which is independent of document semantics, and enhances users' ability to retrieve information. The SeeTrieve system extends this concept to trace the visible text in the GUI with which the user interacts and associate this with files whose accesses occur at proximal points in time. In addition to improving retrieval for users, it creates a form of automated, task-oriented tagging of files. Advisors/Committee Members: Gyllstrom, Karl, Stotts, P. David.

Subjects/Keywords: College of Arts and Sciences; Department of Computer Science

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Gyllstrom, K. (2009). Enriching personal information management with document interaction histories. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:fcdf7b53-b961-45e5-9ee9-94fcdb14597d

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Gyllstrom, Karl. “Enriching personal information management with document interaction histories.” 2009. Thesis, University of North Carolina. Accessed April 13, 2021. https://cdr.lib.unc.edu/record/uuid:fcdf7b53-b961-45e5-9ee9-94fcdb14597d.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Gyllstrom, Karl. “Enriching personal information management with document interaction histories.” 2009. Web. 13 Apr 2021.

Vancouver:

Gyllstrom K. Enriching personal information management with document interaction histories. [Internet] [Thesis]. University of North Carolina; 2009. [cited 2021 Apr 13]. Available from: https://cdr.lib.unc.edu/record/uuid:fcdf7b53-b961-45e5-9ee9-94fcdb14597d.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Gyllstrom K. Enriching personal information management with document interaction histories. [Thesis]. University of North Carolina; 2009. Available from: https://cdr.lib.unc.edu/record/uuid:fcdf7b53-b961-45e5-9ee9-94fcdb14597d

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of North Carolina

3. Miller, Dorian. Can We Work Together?.

Degree: Computer Science, 2009, University of North Carolina

People have a versatility to adapt to various situations in order to communicate with each other regardless of a person's disability. We research separate computer interfaces to support remote synchronous collaboration in two situations. First, a deaf person collaborating with a hearing person uses a shared workspace with video conferencing, such as the Facetop system. Second, a blind person collaborating with a sighted person uses our loosely coupled custom shared workspace called Deep View. The design features of the respective interfaces accommodate the disability of a deaf person or a blind person and enable communication with a person without a disability. The interfaces expand the ways in which people with disabilities participate in a collaborative task to a level of detail not possible without our interfaces. The design features of our user interfaces provide alternative channels for the collaborators with disabilities to communicate ideas or coordinate actions that collaborators without disabilities would otherwise do verbally or visually. We evaluate the interfaces through three user studies where collaborators complete full fledged tasks that require managing all aspects of communication to complete the task. Throughout the research we collaborated with members of the Deaf community and members of the blind community. We incorporated the feedback from members of these communities into the implementation of our interfaces. The members participated in our user studies to evaluate the interfaces. Advisors/Committee Members: Miller, Dorian, Stotts, P. David.

Subjects/Keywords: College of Arts and Sciences; Department of Computer Science

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Miller, D. (2009). Can We Work Together?. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:5035397f-d5f6-4b21-ac90-70851b3f7861

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Miller, Dorian. “Can We Work Together?.” 2009. Thesis, University of North Carolina. Accessed April 13, 2021. https://cdr.lib.unc.edu/record/uuid:5035397f-d5f6-4b21-ac90-70851b3f7861.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Miller, Dorian. “Can We Work Together?.” 2009. Web. 13 Apr 2021.

Vancouver:

Miller D. Can We Work Together?. [Internet] [Thesis]. University of North Carolina; 2009. [cited 2021 Apr 13]. Available from: https://cdr.lib.unc.edu/record/uuid:5035397f-d5f6-4b21-ac90-70851b3f7861.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Miller D. Can We Work Together?. [Thesis]. University of North Carolina; 2009. Available from: https://cdr.lib.unc.edu/record/uuid:5035397f-d5f6-4b21-ac90-70851b3f7861

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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