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Residence of Jno. Sneed Williams
No full texthttps://digitalcommons.memphis.edu/picturing-memphis-images/1154/thumbnail.jp
Residence of B.C. Alsup
No full texthttps://digitalcommons.memphis.edu/picturing-memphis-images/1159/thumbnail.jp
Residence of J.N. Mulford
No full texthttps://digitalcommons.memphis.edu/picturing-memphis-images/1161/thumbnail.jp
Residence of Peter G. Grant
No full texthttps://digitalcommons.memphis.edu/picturing-memphis-images/1173/thumbnail.jp
Graduate School Blog - March 2025 volume 2
No full textThe March 2025 UofM Graduate School Blog – Volume 2.5 offers essential guidance for prospective applicants with “5 Common Graduate School Application Mistakes (and How to Avoid Them),” featuring expert advice from Dr. Deborah Tollefsen, Vice Provost and Dean of the Graduate School. Drawing on over two decades of experience, Dr. Tollefsen outlines key pitfalls such as weak program fit, poor communication with recommenders, procrastination, unpolished documents, and vague academic goals offering actionable strategies to help applicants strengthen their submissions. This issue also features a graduate spotlight on Jagath Chandra Mallam from India, a recent Electrical & Computer Engineering graduate who reflects on how UofM’s specialized curriculum helped him grow academically and professionally. Readers are also reminded of the March 28 ProQuest deadline for thesis and dissertation submission and are encouraged to explore the rich cultural and professional landscape Memphis offers to graduate students
A Linear Programming Bound for Sum-Rank Metric Codes
No full textassociation scheme; eigenvalues; linear programming bound; Sum-rank metric code; sum-rank metric grap
WAVE EQUATION WITH NONLINEAR BOUNDARY DISSIPATION AND BOUNDARY/INTERIOR SOURCES OF CRITICAL EXPONENTS IS ASYMPTOTICALLY FINITE DIMENSIONAL AND SMOOTH
No full textboundary dissipation; finite dimension; quasi-stability; regularity; Wave equatio
Predictors of HPV vaccination coverage among adolescents in Tennessee during the COVID-19 pandemic: A cross-sectional study
No full textHPV vaccine hesitancy; Human papillomavirus (HPV); Vaccination coverage; Vaccine hesitanc
Data Augmentation and Imputation for Sparse Learner Performance Data in Intelligent Tutoring Systems
Get PDFIntelligent Tutoring Systems (ITSs) leverage computer-aided technologies to provide dynamically adaptive instruction tailored to individual learners. Recent advancements in Artificial Intelligence (AI) have further enhanced their capacity to deliver responsive, personalized learning experiences. As learners interact with ITSs by responding to questions, their performance data, such as correct and incorrect answers, become essential for assessing and predicting latent knowledge states through analysis and modeling. However, real-world learner data frequently suffer from sparsity due to incomplete interactions, unanswered questions, and missed attempts. Such missing information poses significant challenges, undermining the accuracy of learner modeling and the effectiveness of personalized instruction. To systematically address these challenges, this dissertation presents comprehensive studies focusing on data augmentation and imputation techniques designed to mitigate data sparsity issues in learner performance data from ITSs. Specifically, three interconnected studies are conducted: (1) a generative data augmentation framework to enrich sparse datasets while preserving essential learning attributes, (2) a tensor-based Generative Adversarial Imputation Network (GAIN) to robustly impute missing data by capturing multidimensional learner interactions, and (3) introducing a global-to-local fine-tuning strategy integrating Bayesian Knowledge Tracing (BKT) derived learner similarity features to enhance the personalization, accuracy, and effectiveness of GAIN-based imputation. These studies systematically explore how advanced generative AI methods can significantly address the sparsity of learner performance data from ITS environments. Initially, we introduce a generative data augmentation framework that employs a three-dimensional (3D) tensor representation to capture learner-question-attempt interactions, integrating tensor factorization for data imputation. Generative Artificial Intelligence (GenAI) models, specifically Vanilla Generative Adversarial Networks (GAN) and Generative Pretrained Transformers (GPT-4o), are leveraged to generate synthetic data tailored to individual clusters of learner performance data across varying sample sizes. These generative techniques effectively enhance sparse datasets without distorting core learner characteristics, validated by rigorous evaluations involving Earth Mover’s Distance (EMD) and bimodality coefficients. Experiments conducted using the AutoTutor Adult Reading Comprehension (ARC) dataset demonstrated the framework\u27s efficacy in realistically expanding the learner performance data, thereby supporting more accurate learner modeling and analysis. Subsequently, a novel tensor-based GAIN is proposed, also utilizing a 3D tensor to represent learner performance data across learners, questions, and attempts. Within this 3-D tensor structure, each learner\u27s performance data are organized as a two-dimensional ``learner image\u27\u27 matrix, mapping questions against attempts across the learner dimension. The GAIN model adopts a GAN-based architecture in which a generator imputes plausible values for missing data, guided by a discriminator that balances observed and predicted data, and further enhanced by a hint mechanism that iteratively refines imputations to accurately reflect authentic learner performance patterns. Experiments using real-world datasets including AutoTutor Adult Reading Comprehension (ARC), ASSISTments, and MATHia, demonstrate significant improvements in imputation accuracy, robustness, and cognitive interpretability compared to traditional methods such as tensor factorization and the GAN variants. Further validation via Bayesian Knowledge Tracing (BKT) confirms that critical learning parameters (e.g., initial learning ability, learning rate, guess rate, and slip rate) are preserved in the imputed data, thus maintaining cognitive fidelity relative to the original sparse data. Lastly, the dissertation culminates in the proposal of an innovative global-to-local fine-tuning strategy for the GAIN-based imputation model. This strategy integrates global pretraining with a similarity-based local fine-tuning process, leveraging BKT-derived cognitive parameters to personalize data imputation for individual learner subgroups. Experimental validation using MATHia datasets demonstrates that this refined approach achieves superior imputation accuracy and stability under diverse sparsity conditions ranging from 10\% to 90\% (with sparsity intentionally created through simulations derived from comprehensive datasets while retaining ground-truth data for validation). The global-to-local fine-tuning strategy effectively aligns imputed data with individualized cognitive trajectories, substantially enhancing the accuracy of imputations by embedding the semantic understanding of learner-specific characteristics into the GAIN-based imputation model. The contributions of this dissertation significantly advance ITSs by introducing robust, innovative methodologies tailored to address data sparsity in learner performance data. These methodologies facilitate the precision of learner modeling and substantially improve the effectiveness of adaptive instructional interventions by closely aligning instructional strategies with learners\u27 cognitive and performance characteristics. Moreover, the scalable and reliable solutions developed herein provide comprehensive, contextually coherent datasets, significantly augmenting the practical utility and effectiveness of ITS environments, thereby promoting educational equity and efficacy through AI-driven personalized learning experiences
DWR Webinar - University Libraries Services & Resources for Graduate Students, GAs and TAs
No full textAshley Roach-Freiman, Research and Instruction Librarian & Associate Professor, shares University Libraries resource and services that are extremely helpful for graduate students, GAs, and TAs