Barrett

Barrett, The Honors College at Arizona State University proudly showcases the work of undergraduate honors students by sharing this collection exclusively with the ASU community.

Barrett accepts high performing, academically engaged undergraduate students and works with them in collaboration with all of the other academic units at Arizona State University. All Barrett students complete a thesis or creative project which is an opportunity to explore an intellectual interest and produce an original piece of scholarly research. The thesis or creative project is supervised and defended in front of a faculty committee. Students are able to engage with professors who are nationally recognized in their fields and committed to working with honors students. Completing a Barrett thesis or creative project is an opportunity for undergraduate honors students to contribute to the ASU academic community in a meaningful way.

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Using Learner Profiles to Create Teachable Tutor Systems

Description
In the 2 Sigma Learning Lab we use Quinn as an embodied agent in the tangible activities for geometry (TAG) system. The TAG system is focused on teaching students basic geometry skills, mainly plotting points in a Cartesian Grid. The TAG system's main tool is Quinn's reactive prompts which hel…

In the 2 Sigma Learning Lab we use Quinn as an embodied agent in the tangible activities for geometry (TAG) system. The TAG system is focused on teaching students basic geometry skills, mainly plotting points in a Cartesian Grid. The TAG system's main tool is Quinn's reactive prompts which help students overcome various misconceptions and teach basic concepts about the Cartesian plane and points. By analyzing past studies using the TAG system my goal is to develop prototypical agent profiles based off of my findings in learner profiles that might facilitate focused tutoring that addresses important misconceptions. Furthermore, applied use of these prototypical agent profiles would allow for students to teach Quinn as an application of the Learning-by-Teaching paradigm.
ContributorsYocky, Jonathan Andrew (Author) / Walker, Erin (Thesis director) / Wetzel, Jon (Committee member) / Computer Science and Engineering Program (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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LudoNarrare: A Model for Verb Based Interactive Storytelling

Description
Instead of providing the illusion of agency to a reader via a tree or network of prewritten, branching paths, an interactive story should treat the reader as a player who has meaningful influence on the story. An interactive story can accomplish this task by giving the player a large toolset…

Instead of providing the illusion of agency to a reader via a tree or network of prewritten, branching paths, an interactive story should treat the reader as a player who has meaningful influence on the story. An interactive story can accomplish this task by giving the player a large toolset for expression in the plot. LudoNarrare, an engine for interactive storytelling, puts "verbs" in this toolset. Verbs are contextual choices of action given to agents in a story that result in narrative events. This paper begins with an analysis and statement of the problem of creating interactive stories. From here, various attempts to solve this problem, ranging from commercial video games to academic research, are given a brief overview to give context to what paths have already been forged. With the background set, the model of interactive storytelling that the research behind LudoNarrare led to is exposed in detail. The section exploring this model contains explanations on what storyworlds are and how they are structured. It then discusses the way these storyworlds can be brought to life. The exposition on the LudoNarrare model finally wraps up by considering the way storyworlds created around this model can be designed. After the concepts of LudoNarrare are explored in the abstract, the story of the engine's research and development and the specifics of its software implementation are given. With LudoNarrare fully explained, the focus then turns to plans for evaluation of its quality in terms of entertainment value, robustness, and performance. To conclude, possible further paths of investigation for LudoNarrare and its model of interactive storytelling are proposed to inspire those who wish to continue in the spirit of the project.
ContributorsStark, Joshua Matthew (Author) / VanLehn, Kurt (Thesis director) / Wetzel, Jon (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12
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Predicting Self-Correction Attempts with FACT, an Automated Teaching Assistant for Algebra Classes

Description

Machine learning is a rapidly growing field, with no doubt in part due to its countless applications to other fields, including pedagogy and the creation of computer-aided tutoring systems. To extend the functionality of FACT, an automated teaching assistant, we want to predict, using metadata produced by student activity, whether…

Machine learning is a rapidly growing field, with no doubt in part due to its countless applications to other fields, including pedagogy and the creation of computer-aided tutoring systems. To extend the functionality of FACT, an automated teaching assistant, we want to predict, using metadata produced by student activity, whether a student is capable of fixing their own mistakes. Logs were collected from previous FACT trials with middle school math teachers and students. The data was converted to time series sequences for deep learning, and ordinary features were extracted for statistical machine learning. Ultimately, deep learning models attained an accuracy of 60%, while tree-based methods attained an accuracy of 65%, showing that some correlation, although small, exists between how a student fixes their mistakes and whether their correction is correct.
ContributorsZhou, David (Author) / VanLehn, Kurt (Thesis director) / Wetzel, Jon (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05