Digital Commons Network^™

Meeting Minutes 05-21-2025, Staff Advisory Council

Staff Advisory Council Meeting Documents

No abstract provided.

Rose, The Commissioner, And The Hall, Richard C. Crepeau

On Sport and Society

It seems that the biggest news in baseball this week is off the field. Usually, that is not a good thing. Yesterday, the Commissioner of Baseball, Rob Manfred, announced that Pete Rose and sixteen other players have been re-instated from baseball’s “permanently ineligible” list. Among the sixteen others are several players caught up in the Black Sox scandal of 1919.

Handwritten Digit Recognition Using Machine Learning, Dipok Deb

Data Science and Data Mining

Handwritten Digit Recognition (HDR) remains a fundamental benchmark in pattern recognition and machine learning due to its practical applications and inherent classification challenges posed by diverse handwriting styles. This study investigates and compares two classical statistical classifiers—Gaussian Naive Bayes (GNB) and Linear Discriminant Analysis (LDA)—to recognize the digits from the MNIST dataset. Both models assume underlying normality in feature distributions and offer computational efficiency, making them suitable for high-dimensional input such as image pixels. Using 60,000 training and 10,000 test samples, we evaluate model performance through accuracy, precision, recall, F1 score, and confusion matrices. The results reveal that while GNB …

Clustering Dataset Using K-Mean Clustering, Dipok Deb

Data Science and Data Mining

Clustering is a fundamental technique in unsupervised machine learning, widely applied in various domains such as pattern recognition, data segmentation, and anomaly detection. This study evaluates the performance of the K-Means clustering algorithm on multiple benchmark datasets, including low-dimensional, high-dimensional, and imbalanced datasets. The clustering results are assessed using four key evaluation metrics: Mean Squared Error (MSE), Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), and Silhouette Score. Experimental results demonstrate that K-Means performs effectively on datasets with well-separated clusters, particularly in high-dimensional spaces, where it achieves near-perfect clustering accuracy. However, its performance deteriorates in datasets with overlapping clusters and …

Using Generative Ai To Enrich Instructional Videos With Embedded Quizzes, Efren De La Mora Velasco, Shaurya Agarwal

Teaching Online Pedagogical Repository

This entry describes a strategy to enhance student engagement in asynchronous online courses by embedding AI-generated quizzes into instructional videos. Using ChatGPT and video transcripts, the instructor created targeted multiple-choice questions that aligned with key video topics and improved interactivity. Results from the CGN5341 course showed increased video view duration, higher engagement, and positive student feedback on the usefulness of short, segmented videos with embedded quizzes.

Florida Fish And Wildlife Conservation Commission Comment Tool For Proposed Changes To Bear Hunting Regulations, Amy Donley, Jacquelyn Reiss, Alexandria Mcclarty, Elise Lorenzo

Institute for Social and Behavioral Science (ISBS)

No abstract provided.

Win-Win Field Placement: Creating Research Innovations In Online Msw Programs, Christina Kuchman, Hsiu-Fen Lin

Teaching Online Pedagogical Repository

This win-win field placement project proposes an innovative research education for the online Master of Social Work (MSW) programs. Students usually have challenges in fulfilling the competency requirement on research and feel disconnected between research, practice, and policy (Betts et al., 2024). Through this innovation, we can unleash the potential of the student, the agency, and the university. More importantly, we believe that this approach can be applied to other majors that require a field education and a research methods course as part of their curriculum, such as education, public health, criminal justice, and environmental science.

Applying Universal Design For Learning Strategies To Increase Student Engagement With Your Course Syllabus, Amy Sugar, Amanda Groff

Teaching Online Pedagogical Repository

Apply Universal Design for Learning to syllabi design.

Creating Smaller, Stable Discussion Communities Within Larger Online Courses, Mary Adler

Teaching Online Pedagogical Repository

Given the adverse effects of large online discussions for student engagement and learning, the entry offers a strategy to establish smaller communities of learners by utilizing stable group sets for a series of learning logs. Steps include soliciting student input on their interests; grouping sets to join together students with common interests; using community-building prompts that include icebreakers, connection to lived experience, and soliciting thoughtful responses; and providing faculty feedback and modeling. Students were enthusiastic about the approach in survey data, with 100% recommending continued use, either as is (76%) or with minor changes (24%).

Comparative Evaluation Of Linear Regression, Cross Validation And Regularization Approaches In Multivariate Data Analysis, Ransford Owusu, Felix Yeboah, Francis Effah Boateng

Data Science and Data Mining

This study evaluates linear regression and its enhanced variants incorporating cross-validation and regularization techniques for high-dimensional, multivariate datasets. We address challenges such as multicollinearity and overfitting. Methods including Ridge, LASSO, and Elastic Net are compared against ordinary least squares regression. Empirical analysis using an automobile dataset for fuel efficiency prediction shows that while OLS regression captures basic relationships, its limitations are mitigated through regularization and cross-validation, resulting in improved model interpretability. The findings provide a comprehensive framework for predictive modeling in complex data environments and offer insights into statistical methodology and practical applications in the automobile industry.

Building And Defending Lgbtq+ Collections, Jason D. Phillips, Jordan Ruud

Faculty Scholarship and Creative Works

This chapter explores the critical role of collection development policies in libraries, particularly in supporting and defending LGBTQ+ collections. The authors argue that a robust, regularly updated, and transparent collection development policy is essential for ensuring equitable representation and resisting censorship. They emphasize that such policies must account for the needs of historically underserved communities, including LGBTQ+ populations, and serve as tools for advocacy and defense against internal and external challenges.

Using real-world examples, the chapter highlights the consequences of outdated or ineffective policies, including instances of hidden censorship and administrative interference in material acquisition. The authors advocate for retrospective …

At What Cost? The Personal Stakes Of Intellectual Freedom, Jason D. Phillips, Jordan Ruud

Faculty Scholarship and Creative Works

This chapter explores the emotional, professional, and political costs faced by librarians advocating for LGBTQ+ inclusion and intellectual freedom within libraries. Through personal narrative and critical reflection, the authors introduce the concept of "queer battle fatigue"—a framework for understanding the cumulative psychological toll on LGBTQ+ individuals who are consistently placed in advocacy roles within predominantly cisheteronormative institutions. A case study of a drag storytime event at a university library illustrates the social backlash, institutional pressures, and moral injury experienced by organizers, demonstrating how well-intentioned programming can become flashpoints for political controversy. The authors highlight the intensifying hostility toward LGBTQ+ representation …

The Scoop, Vol. 11 Issue 11, February 2025, Health Sciences Library

Volume 11

Latest news and updates from the Health Sciences Library in our monthly newsletter for February 2025. Please see page 2 for a text-only version of this issue!

Enemies Within The Gates: Contending With Internal Censorship Challenges, Jason D. Phillips

Faculty Scholarship and Creative Works

This chapter explores the rarely discussed but impactful issue of internal censorship within academic libraries, focusing on challenges to LGBTQ+ materials. Drawing on personal experiences at a regional public university in the American South, the author recounts multiple instances where library staff and leadership acted in ways that undermined the inclusion of LGBTQ+ resources. These incidents ranged from quietly removing or hiding books to resisting the addition of LGBTQ+ periodicals and graphic novels, and even denying purchase requests without explanation. The chapter underscores how fear of controversy, personal bias, and institutional conservatism can result in "soft censorship," despite the profession's …

Comparison Of Two Strategies Of Screening Experiments: Single-Shot Experiment Vs. Two-Stage Screening Experiment, Kelvin Njuki, Emil Agbemade

Data Science and Data Mining

Experiments involving many factors are often complex, time-consuming, and expensive. Screening out the least important factors helps the experimenter(s) allocate the limited resources efciently to the most important factors. Supersaturated and orthogonal array designs are among the designs used to conduct screening experiments. Supersaturated designs (SSDs) are those where the number of runs (observations) is less than the number of factors, while orthogonal array (OA) designs are those where at least the columns are orthogonal to each other. In this study, we conduct a simulation study to compare two strategies of screening experiments. Strategy one is a single shot experiment …

Advanced Machine Learning Techniques For Cardiovascular Disease Risk Prediction, Godfred Ahenkroa Kesse

Data Science and Data Mining

of mortality, necessitating advanced predictive models to aid early detection and prevention. This study explores the application of machine learning techniques, including Lo- gistic Regression, K-Nearest Neighbors (KNN), Random Forest, and XGBoost, to predict CVD risk using a dataset of 69,997 observations encompassing demographic, clinical, and lifestyle factors. Data preprocessing involved one-hot encoding of cat- egorical variables and scaling to ensure compatibility with all models. Model performance was evaluated using metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. Among the models, XGBoost demonstrated the highest accuracy at 74%, leveraging its gradient-boosting framework to effectively handle feature interactions and imbalanced …

Handwritten Digit Recognition Using Naive Bayes And K-Nearest Neighbor Models, Godfred Ahenkroa Kesse

Data Science and Data Mining

This paper explores the performance of two fundamental classifcation algorithms. It uses Naive Bayes and K-Nearest Neighbors (KNN), framing it within the context of digit recognition of the MNIST dataset. The MNIST dataset has 70,00 grayscale images of handwritten digits, offering a standard for assessing classifcation models. This paper focuses on key performance metrics such as precision, accuracy, recall, and F1score to examine the effciency of each model. The results reveal that Naive Bayes has moderate accuracy and misclassifcations because of its notion of feature independence. The paper concludes that the KNN model performs better with the optimal k-value of …

Variable Selection Using Lasso Regression, Godfred Ahenkroa Kesse

Data Science and Data Mining

This study employs Lasso regression to analyze highdimensional genetic data for predicting flowering time in maize, specifically Days to Anthesis (DtoA). Lasso, or Least Absolute Shrinkage and Selection Operator, is a form of linear regression that introduces an L1 penalty to the model, encouraging sparsity by shrinking some coefficients to zero. This attribute makes Lasso ideal for feature selection in large datasets, as it highlights the most influential predictors while discarding irrelevant variables. Unlike Ridge regression, which applies an L2 penalty to minimize the squared magnitude of coefficients, Lasso’s L1 penalty induces sparsity, providing a clearer interpretation of the selected …

Ucf Pegasus Plan - 1st Grade - Maps & Globes, Cecilia Nuss, Mackenzie Luley, Jessenia Diaz, Alyssa Arnott, Miranda Howley

Pegasus Plans: Social Studies Unit Plans for Florida Teachers

During this unit, students will explore and create various maps and globes through multimedia resources, Hands-on activities, and assessments. The daily lessons cover topics such as a compass rose, cardinal directions, map titles, and physical features on maps and globes. By the end of this unit, students will be able to identify key elements and locate physical features of maps and globes. Through the use of songs, hands-on activities, games, books, and class discussions, students will be engaged throughout the unit.

Case Report: Adult Patient With Acquired Apraxia Of Speech Secondary To A Stroke In Broca's Area, Richard Zraick

UCF Created OER Artifacts

This open educational resource (OER) is a case report about an adult patient with acquired apraxia of speech secondary to stroke in Broca's area. This case report was originally developed for SPA 6236: Motor Speech Disorders, School of Communication Sciences & Disorders, University of Central Florida, by Richard Zraick, Ph.D., CCC-SLP, F-ASHA. The content was based on output from ChatGPT and generated with the prompt: “Create a fictional case report for an adult patient with acquired apraxia of speech secondary to cerebrovascular accident in Broca’s area.” Others are free to reuse this OER to distribute, remix, adapt, and build upon …

Mnemonic To Paraphrase A Source And Cite In Apa 7th Edition, Lily Dubach

UCF Created OER Artifacts

This open educational resource illustrates the input-output flow from a prompt typed into ChatGPT 4o (a generative AI developed by OpenAI) to elicit a mnemonic to help students understand how to paraphrase a source and cite it in APA 7^th edition.

Advisor And Counselor Perceptions Within A Southeastern State’S Private Institutions: A Qualitative Exploratory Case Study, Thomas A. Range

Graduate Thesis and Dissertation post-2024

U.S. Black American college students identifying as first generation and experiencing lower socioeconomic status have lagged behind White college students from more affluent families in retention, graduation, and postgraduation metrics. These differences are particularly acute for students attending private, predominantly White institutions. However, there is a gap in knowledge about how student services personnel—whose roles directly impact students’ perseverance toward graduation and their postgraduation plans—are perceived. Many external barriers (e.g., inadequate student support; racial discrimination; counselor, advisor, administrator, and college leadership biases) have continued to hinder success of Black American collegians in private institutions. To focus this study, two guiding …

Qualities Of Instructors Adult Second Language Learners Identify As Being Most Effective In Supporting Their Language Skills In Intensive English Programs, Nick Myrca Gauthier

Graduate Thesis and Dissertation post-2024

This qualitative research discusses the importance of the pedagogical and social qualities of instructors in the academic achievement of adult second language (L2) learners enrolled in intensive English programs (IEP). Its primary goal is to comprehend the role of social traits such as empathy, patience, approachability, supportiveness, and respectfulness and of pedagogical traits such as building trust, motivation, and minimizing language learning anxiety when instructing adult L2 learners. Within the framework of this study, a semistructured interview format was used to gather the lived experiences of seven participants who were either enrolled or had been enrolled in a university’s intensive …

Handwritten Digit Recognition Using Machine Learning Classifiers, Md Ahiduzzaman

Data Science and Data Mining

This project explores and compares the performance of various machine learning classifiers for handwritten digit recognition using the MNIST dataset. The classifiers include Logistic Regression, k-Nearest Neighbors, and Convolutional Neural Networks. Each classifier is evaluated based on accuracy, precision, recall, F1-score, and confusion matrix analysis.

Journeys Across Worlds, Carolina Guzman

Graduate Thesis and Dissertation post-2024

Journeys across Worlds is a collection of five short stories that transport readers into otherworldly landscapes. Each narrator embarks on a transformative journey, encountering adventure, wonder, and self-discovery. In “Through Borrowed Eyes,” a blind sorceress wields a powerful curse to see through the eyes of others, only to realize the cost of her magic may be too great. “A Gaze Across Dreams” follows a lonely Dreamweaver who, after an eternity of being unseen, is suddenly perceived by a lucid dreamer. The collection concludes with “Where the Skies Are Blue,” a tale of two sisters navigating a post-apocalyptic world, where the …

Sliding Through The Cracks: An Evaluation Of The Fba System And An Exploration Of Traditional Trombone Training Practices, Simon Jones

Graduate Thesis and Dissertation post-2024

My experience as a low brass player trained in an FBA (Florida Bandmasters Association) program was well rounded. I attended a school with a rich history of continued excellence, taking part in various honor bands and preparing standard trombone repertoire for the Solo and Ensemble evaluations. Despite all of this, fundamental aspects of my trombone playing were lacking when I arrived at college, remaining unrectified until I started my master’s degree at UCF. I was a committed trombone player, regularly practicing multiple hours every day during my formative years. In retrospect, I realize that my performances suffered in fundamental areas. …

The Unindexed Marginalia Of Historical American Cookery Books Digital Public History Project: Domestic Technical Communication Labor Contributions, Alessandra Zinicola Lopez

Graduate Thesis and Dissertation post-2024

This dissertation includes a traditional written portion, and a non-traditional digital humanities public history online digital archive project created through Omeka which showcases examples of hand-written recipes found within commercial cookbooks, called marginalia. The understanding of modern technical communication is affected by the perceived history of the field and currently, that history is biased toward non-domestic pursuits. The project explores what collecting, digitizing, and archiving examples of historical hand-written marginalia found within commercially printed American receipt books indicate about the presence, participatory nature, value, and contributions of domestic technical communications and their user-authors in the 19th century. The examples collected …

Multidimensional Approaches In Bug Detection For Parallel Programming And Text-To-Code Semantic Parsing, May Alsofyani

Graduate Thesis and Dissertation post-2024

This dissertation applies deep learning and large language models to two domains: parallel programming fault detection and text-to-code translation, aiming to enhance software reliability and natural language-driven code generation. Due to their unpredictable nature, concurrency bugs-particularly data race bugs— present significant challenges in fault detection for parallel programming. We investigate deep learning and LLM-based approaches for detecting data race bugs in OpenMP programs. Our proposed methods include a transformer encoder and GPT-4 through prompt engineering and fine-tuning. Experimental results demonstrate that the transformer encoder achieves competitive accuracy compared to LLMs, highlighting its effectiveness in understanding complex OpenMP directives. Expanding this …

Weakly-Supervised Scaling For Open-Vocabulary Action Detection, Zhen Hao Sia

Graduate Thesis and Dissertation post-2024

In this work, we focus on scaling open-vocabulary action detection. Existing approaches for action detection are predominantly limited to closed-set scenarios and rely on complex, parameter-heavy architectures. Extending these models to the open-vocabulary setting poses two key challenges: (1) the lack of large-scale datasets with many action classes for robust training, and (2) parameter-heavy adaptations to a pretrained vision-language contrastive model to convert it for detection, risking overfitting the additional non-pretrained parameters to base action classes.

Firstly, we introduce an encoder-only multimodal model for video action detection, reducing the reliance on parameter-heavy additions for video action detection. Secondly, we introduce …

Developing Real-Time Crash Prediction System Using Ai-Based Methods On Interstate Incorporating Express Lanes, Samgyu Yang

Graduate Thesis and Dissertation post-2024

This research develops a real-time crash prediction system that integrates machine learning techniques and real-time data to forecast crash likelihood across various road segments. The system particularly addresses newly constructed Interstate 4 Express managed lanes, modeling them separately due to their unique challenges, such as limited crash data and new road designs. By treating these segments individually, the system ensures more accurate predictions that account for their distinct traffic behaviors. By leveraging anomaly detection learning (ADL), the system identifies rare crash events by detecting deviations from normal traffic behavior, even with imbalanced data. ADL is applied specifically to the influenced …