Master of Fine Arts, The Ohio State University, 2024, Design

Endogenously educational games seek to embed real-world or educational content into every game element, including the gameplay mechanics. Designing mechanics that are both fun and accurate can be a challenge. It becomes more difficult when the subject matter is both complex and unfamiliar to the game designer. In this paper, I explore this design challenge through a case study of a year-long interdisciplinary board game design project about watershed management in the Lake Erie basin. I examine the participation and contributions of a subject matter expert to the project. I discuss the challenges of adapting content while making gameplay changes. I then propose a recommended framework for future designers who want to create endogenously educational games collaboratively.

Committee: Scott Swearingen (Advisor); Kyoung Swearingen (Committee Member); Ruth Smith (Committee Member); Maria Palazzi (Committee Member) Subjects: Design

Doctor of Philosophy, The Ohio State University, 2012, Industrial and Systems Engineering

We compare probabilistic clustering methods for analyzing unstructured text or images relevant to prioritizing system improvement actions. Such system improvement activities require an awareness of the entire corpus or set of documents such as transcripts of phone conversations or images. For example, a manager trying to improve the performance of a call center might want to quantitatively understand what the fractions of calls are of a set of types (cluster or topic proportions) and what those types are including the phrases associated phrases (cluster or topic definitions). If a sizable fraction of conversations, e.g., 15%, were using unapproved language, there could be a high priority on implementing standardization or training to reduce cost and improve customer satisfaction related to the identified cluster or topic. We argue that such prioritization could be best understood only if proportions and definitions of all of the clusters or topics can be accounted for accurately. The goal of accurate accounting for the entire corpus is different from information retrieval goals. Information retrieval relates to identifying specific documents of interest in specific queries. As a result, our comparison is based on “ground truth” models of four entire corpora and four measures of distribution fitting accuracy. Yet, the literature on numerical and case study comparisons of probabilistic clustering methods for cases with ground truth standards is lacking. Benefits of comparisons based on ground truth models and given corpora also include the provision of complete examples so that readers can see clearly how different approaches can be applied. Further, using the accuracy of cluster identification permits the comparison of popular methods such as fuzzy clustering together with generative methods such as Bayesian mixture models. This is true as long as we interpret the fuzzy clustering model as a topic model which we do. The resulting “fuzzy topic models” offer demonstra (open full item for complete abstract)

Committee: Theodore Allen (Advisor); Cathy Xia (Committee Member); Clark Mount-Campbell (Committee Member); Bruce Patton (Committee Member) Subjects: Industrial Engineering

Doctor of Philosophy, The Ohio State University, 2011, Industrial and Systems Engineering

Engineers face many quality-related datasets containing free-style text or images. For example, a database could include summaries of complaints filed by customers, or descriptions of the causes of rework or maintenance or of the associated actions taken, or a collection of quality inspection images of welded tubes. The goal of this dissertation is to enable engineers to input a database of free-style text or image data and then obtain a set of clusters or “topics” with intuitive definitions and information about the degree of commonality that together helps prioritize system improvement. The proposed methods generate Pareto charts of ranked clusters or topics with their interpretability improved by input from the analyst or method user. The combination of subject matter expert data with standard data is the novel feature of the methods considered. Prior to the methods proposed here, analysts applied Bayesian mixture models and had limited recourse if the cluster or topic definitions failed to be interpretable or are at odds with the knowledge of subject matter experts. The associated “Subject Matter Expert Refined Topic” (SMERT) model permits on-going knowledge elicitation and high-level human expert data integration to address the issues regarding: (1) unsupervised topic models often produce results to user, and (2) to provide a “Hierachical Analysis Designed Latency Experiment” (HANDLE) for human expert to interact with the model results. If grouping are missing key elements, so-called “boosting” these elements is possible. If certain members of a cluster are nonsensical or nonphysical, so-called “zapping” these nonsensical elements is possible. We also describe a fast Collapsed Gibbs Sampling (CGS) algorithm for SMERT method, which offers the capacity to efficiently SMERT model large datasets but which is associated with approximations in certain cases. We use three case studies to illustrate the proposed methods. The first relates to scrap text reports for a Ch (open full item for complete abstract)

Committee: Theodore Allen PhD (Advisor); Suvrajeet Sen PhD (Committee Member); David Woods PhD (Committee Member) Subjects: Computer Science; Engineering; Industrial Engineering; Information Technology