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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 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
Description
Online learning communities have changed the way users learn due to the technological affordances web 2.0 has offered. This shift has produced different kinds of learning communities like massive open online courses (MOOCs), learning management systems (LMS) and question and answer based learning communities. Question and answer based communities are an important part of social information seeking. Thousands of users participate in question and answer based communities on the web like Stack Overflow, Yahoo Answers and Wiki Answers. Research in user participation in different online communities identifies a universal phenomenon that a few users are responsible for answering a high percentage of questions and thus promoting the sustenance of a learning community. This principle implies two major categories of user participation, people who ask questions and those who answer questions. In this research, I try to look beyond this traditional view, identify multiple subtler user participation categories. Identification of multiple categories of users helps to provide specific support by treating each of these groups of users separately, in order to maintain the sustenance of the community.
In this thesis, participation behavior of users in an open and learning based question and answer community called OpenStudy has been analyzed. Initially, users were grouped into different categories based on the number of questions they have answered like non participators, sample participators, low, medium and high participators. In further steps, users were compared across several features which reflect temporal, content and question/thread specific dimensions of user participation including those suggestive of learning in OpenStudy.
The goal of this thesis is to analyze user participation in three steps:
a. Inter group participation analysis: compare pre assumed user groups across the participation features extracted from OpenStudy data.
b. Intra group participation analysis: Identify sub groups in each category and examine how participation differs within each group with help of unsupervised learning techniques.
c. With these grouping insights, suggest what interventions might support the categories of users for the benefit of users and community.
This thesis presents new insights into participation because of the broad range of
features extracted and their significance in understanding the behavior of users in this learning community.
In this thesis, participation behavior of users in an open and learning based question and answer community called OpenStudy has been analyzed. Initially, users were grouped into different categories based on the number of questions they have answered like non participators, sample participators, low, medium and high participators. In further steps, users were compared across several features which reflect temporal, content and question/thread specific dimensions of user participation including those suggestive of learning in OpenStudy.
The goal of this thesis is to analyze user participation in three steps:
a. Inter group participation analysis: compare pre assumed user groups across the participation features extracted from OpenStudy data.
b. Intra group participation analysis: Identify sub groups in each category and examine how participation differs within each group with help of unsupervised learning techniques.
c. With these grouping insights, suggest what interventions might support the categories of users for the benefit of users and community.
This thesis presents new insights into participation because of the broad range of
features extracted and their significance in understanding the behavior of users in this learning community.
ContributorsSamala, Ritesh Reddy (Author) / Walker, Erin (Thesis advisor) / VanLehn, Kurt (Committee member) / Hsieh, Gary (Committee member) / Wetzel, Jon (Committee member) / Arizona State University (Publisher)
Created2015