Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
Displaying 1 - 3 of 3
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- All Subjects: Educational technology
- All Subjects: software product lines
- Creators: Burleson, Winslow
Description
Lots of previous studies have analyzed human tutoring at great depths and have shown expert human tutors to produce effect sizes, which is twice of that produced by an intelligent tutoring system (ITS). However, there has been no consensus on which factor makes them so effective. It is important to know this, so that same phenomena can be replicated in an ITS in order to achieve the same level of proficiency as expert human tutors. Also, to the best of my knowledge no one has looked at student reactions when they are working with a computer based tutor. The answers to both these questions are needed in order to build a highly effective computer-based tutor. My research focuses on the second question. In the first phase of my thesis, I analyzed the behavior of students when they were working with a step-based tutor Andes, using verbal-protocol analysis. The accomplishment of doing this was that I got to know of some ways in which students use a step-based tutor which can pave way for the creation of more effective computer-based tutors. I found from the first phase of the research that students often keep trying to fix errors by guessing repeatedly instead of asking for help by clicking the hint button. This phenomenon is known as hint refusal. Surprisingly, a large portion of the student's foundering was due to hint refusal. The hypothesis tested in the second phase of the research is that hint refusal can be significantly reduced and learning can be significantly increased if Andes uses more unsolicited hints and meta hints. An unsolicited hint is a hint that is given without the student asking for one. A meta-hint is like an unsolicited hint in that it is given without the student asking for it, but it just prompts the student to click on the hint button. Two versions of Andes were compared: the original version and a new version that gave more unsolicited and meta-hints. During a two-hour experiment, there were large, statistically reliable differences in several performance measures suggesting that the new policy was more effective.
ContributorsRanganathan, Rajagopalan (Author) / VanLehn, Kurt (Thesis advisor) / Atkinson, Robert (Committee member) / Burleson, Winslow (Committee member) / Arizona State University (Publisher)
Created2011
Description
For this master's thesis, a unique set of cognitive prompts, designed to be delivered through a teachable robotic agent, were developed for students using Tangible Activities for Geometry (TAG), a tangible learning environment developed at Arizona State University. The purpose of these prompts is to enhance the affordances of the tangible learning environment and help researchers to better understand how we can design tangible learning environments to best support student learning. Specifically, the prompts explicitly encourage users to make use of their physical environment by asking students to perform a number of gestures and behaviors while prompting students about domain-specific knowledge. To test the effectiveness of these prompts that combine elements of cognition and physical movements, the performance and behavior of students who encounter these prompts while using TAG will be compared against the performance and behavior of students who encounter a more traditional set of cognitive prompts that would typically be used within a virtual learning environment. Following this study, data was analyzed using a novel modeling and analysis tool that combines enhanced log annotation using video and user model generation functionalities to highlight trends amongst students.
ContributorsThomas, Elissa (Author) / Burleson, Winslow (Thesis advisor) / Muldner, Katarzyna (Committee member) / Walker, Erin (Committee member) / Glenberg, Arthur (Committee member) / Arizona State University (Publisher)
Created2014
Description
Affect signals what humans care about and is involved in rational decision-making and action selection. Many technologies may be improved by the capability to recognize human affect and to respond adaptively by appropriately modifying their operation. This capability, named affect-driven self-adaptation, benefits systems as diverse as learning environments, healthcare applications, and video games, and indeed has the potential to improve systems that interact intimately with users across all sectors of society. The main challenge is that existing approaches to advancing affect-driven self-adaptive systems typically limit their applicability by supporting the creation of one-of-a-kind systems with hard-wired affect recognition and self-adaptation capabilities, which are brittle, costly to change, and difficult to reuse. A solution to this limitation is to leverage the development of affect-driven self-adaptive systems with a manufacturing vision.
This dissertation demonstrates how using a software product line paradigm can jumpstart the development of affect-driven self-adaptive systems with that manufacturing vision. Applying a software product line approach to the affect-driven self-adaptive domain provides a comprehensive, flexible and reusable infrastructure of components with mechanisms to monitor a user’s affect and his/her contextual interaction with a system, to detect opportunities for improvements, to select a course of action, and to effect changes. It also provides a domain-specific architecture and well-documented process guidelines, which facilitate an understanding of the organization of affect-driven self-adaptive systems and their implementation by systematically customizing the infrastructure to effectively address the particular requirements of specific systems.
The software product line approach is evaluated by applying it in the development of learning environments and video games that demonstrate the significant potential of the solution, across diverse development scenarios and applications.
The key contributions of this work include extending self-adaptive system modeling, implementing a reusable infrastructure, and leveraging the use of patterns to exploit the commonalities between systems in the affect-driven self-adaptation domain.
This dissertation demonstrates how using a software product line paradigm can jumpstart the development of affect-driven self-adaptive systems with that manufacturing vision. Applying a software product line approach to the affect-driven self-adaptive domain provides a comprehensive, flexible and reusable infrastructure of components with mechanisms to monitor a user’s affect and his/her contextual interaction with a system, to detect opportunities for improvements, to select a course of action, and to effect changes. It also provides a domain-specific architecture and well-documented process guidelines, which facilitate an understanding of the organization of affect-driven self-adaptive systems and their implementation by systematically customizing the infrastructure to effectively address the particular requirements of specific systems.
The software product line approach is evaluated by applying it in the development of learning environments and video games that demonstrate the significant potential of the solution, across diverse development scenarios and applications.
The key contributions of this work include extending self-adaptive system modeling, implementing a reusable infrastructure, and leveraging the use of patterns to exploit the commonalities between systems in the affect-driven self-adaptation domain.
ContributorsGonzalez-Sanchez, Javier (Author) / Burleson, Winslow (Thesis advisor) / Collofello, James (Thesis advisor) / Garlan, David (Committee member) / Sarjoughian, Hessam S. (Committee member) / Atkinson, Robert (Committee member) / Arizona State University (Publisher)
Created2016