Digital Commons Network^™

Grades Are Bugs, Jordan Freitas

Computer Science Faculty Works

This paper argues that grades are bugs in our educational system, undermining desired behaviors and outcomes. Grades were introduced into higher education for purposes directly at odds with the goals of inclusive pedagogy today, as well as the neuroscience of human motivation and learning. Students enter computer science programs from increasingly varied backgrounds and experiences, and face a rapidly evolving landscape of prospective career paths while higher education costs in the United States are ever increasing. Computer science educators have a responsibility to adapt and carefully re-examine typical approaches to all aspects of the learning environments we build and curricula …

Refinement Or Disruption: Patterns Of Critical Inquiry In Computing Ethics Assignments, Vincent Barfield, Charles Wallace

Michigan Tech Publications

Ethical inquiry in computing has an evolving nature, where repeated critical questioning may lead to a series of reframings of the problem. Since many computing students have experience with agile project-based courses, an iterative approach can provide a familiar entry into ethics. Agile development, however, is typically oriented around satisfaction of customer goals, while ethical inquiry requires constant questioning that may cast those goals into doubt. Does the similarity between critical ethical inquiry and iterative development provide a helpful model or breed a false familiarity? To address this question, we examine student work in a computing ethics course, involving iterative …

An Exploratory Analysis Of Automated Deception Detection For Mental Health Applications, Sayde Leya King

USF Tampa Graduate Theses and Dissertations

Deception in mental health settings can undermine therapeutic relationships, compromise treatment efficacy, and impact patient outcomes. Yet, research shows that mental health clinicians often perform no better than chance at detecting deceptive behavior in therapy. Automated deception detection, leveraging artificial intelligence (AI) and multimodal behavioral cues—such as eye gaze, body gestures, and facial expressions—offers a promising alternative. However, most existing research focuses on high-stakes legal contexts, limiting its applicability to mental health settings.

This dissertation addresses this gap by pursuing three key research objectives using a mixed-methods approach. First, we investigate mental health clinicians’ perspectives on AI-assisted deception detection through …

Technology Manufacturing Organizations’ Strategies For Achieving Sustainability, Kate Cheney

Walden Dissertations and Doctoral Studies

Sustainability remains a challenge for technology manufacturers struggling to integrate environmental priorities into core operations. This issue is critical for technology manufacturing managers who must embed sustainability into organizational systems and decision-making processes to meet rising expectations from regulators, investors, and consumers. Grounded in transformational leadership theory, the purpose of this qualitative pragmatic inquiry study is to identify and explore effective strategies used by technology manufacturing managers in the western region of the United States. The data collected from seven semistructured interviews were analyzed using thematic analysis. The five themes that emerged were (a) leadership and company culture, (b) collaboration, …

Homework, Households, And Hurdles: The Unexpected Drivers Of Student Graduation Perceptions, Daniel Alhassan, Zahra Fatah, Priscilla Mansah Codjoe, Caroline Bena Kuno, Dorcas Ofori-Boateng

Mathematics and Statistics Faculty Publications and Presentations

Students’ perceptions of their likelihood to graduate are important determinants of their academic engagement, yet these perceptions remain understudied. This study, which is based on the 2021 Monitoring the Future survey of 8th- and 10th-grade students in the United States, uses machine learning algorithms to identify the most important factors that influence these perceptions. Among the tested models, random forest provided the best classification performance. Using permutation-based feature importance, we identified frequent participation in schoolwork, maternal education, paternal education, and homework completion as the most important predictors of students’ graduation perceptions. These results highlight the importance of targeted and well-coordinated …

Removal Of Heavy Metals From Motorcycle Washing Wastewater Using A Zeolite-Embedded Sheet, Noverita Dian Takarina, Hanies Ambarsari, Sekar Arum Pramudyawardhani, Maria Fatima Niken Kris Rahmawati, Erni Johan, Naoto Matsue, Mohammad Afdhal Adidharma

Makara Journal of Science

The increased use of motorcycles for transportation has heightened environmental challenges related to their washing, particularly the discharge of untreated wastewater containing heavy metals, such as copper (Cu) and lead (Pb), into drainage systems. This study explored the effectiveness of zeolite-embedded sheets (ZESs) in the removal of these pollutants from motorcycle washing wastewater. ZESs were prepared through treatment of nonwoven sheets with Linder Type A (LTA Zeolite) powder and heat treatment, washing, and drying. Characterization was performed via scanning electron microscopy with energy-dispersive X-ray spectroscopy, and Fourier transform infrared spectroscopy. Over a 60 min adsorption period, the ZES achieved removal …

Stability Analysis And Extension Of A Discrete-Time Dynamical System For Scaffolded Learning, Kris H. Green, Bernard Ricca

Northeast Journal of Complex Systems (NEJCS)

Van Geert and Steenbeek [16] proposed a coupled, delayed, discrete-time deterministic dynamical system to model scaffolded learning. We provide a detailed analysis of their model, whose global dynamics are complicated by the presence of intersecting lines of non-isolated, nonhyperbolic fixed points. We also interpret some of the trajectories in the system that have interesting dynamics in the context of the teacher-student interactions, and propose an extension to the model that simultaneously collapses the lines of fixed points to single, isolated points and is easily interpreted. These results provide the foundation for guiding the collection and integration of experimental data to …

Exploring The Potential Of Large Language Models (Llms) To Simulate Social Group Dynamics: A Case Study Using The Board Game "Secret Hitler", Kaj Hansteen Izora, Christof Teuscher

Northeast Journal of Complex Systems (NEJCS)

This study explores the capacity of large language model-powered agents to simulate human-like behavior in multi-agent social systems. Using Secret Hitler — a hidden-role board game centered on trust, deception, and strategic communication — we evaluate how LLM agents navigate dynamic group interactions. Our findings show that agents exhibit human-like behaviors, including strategic temporal adaptation, contextual reasoning, and complex social cognition such as theory of mind and implicit coordination. Notably, 85% of agent decisions factored in at least two other players’ mental states, highlighting their capacity for multi-agent mental state inference. However, they struggled with key aspects of human gameplay, …

Advancing Political Science With Machine Learning: A Gaussian Process Approach, Yehu Chen

McKelvey School of Engineering Graduate Student Theses & Dissertations

The proliferation of data in recent decades including including survey, image and text data, has significantly transformed the landscape of political science research. Machine learning methods have played an instrumental role in analyzing these datasets, yet their application poses challenges in areas where political concepts are not directly measurable or the primary focus of inference is causality. In addition, the essence of machine learning algorithms being trained for prediction performance in a black-boxed manner, makes their outputs hardly interpretable and even unappreciated. This dissertation addresses these issues by proposing a novel methodological framework that employs Gaussian Process (GP) models, a …

Classification Of Human Trust In Ai Using Brain Activity Data, Danushka Bandara, Ruhuan Liao, Fatima Chowdhury, Leslie Abbott

Northeast Journal of Complex Systems (NEJCS)

Trust plays a crucial role in human-computer interaction, particularly in scenarios involving artificial intelligence (AI) systems. This study explores the feasibility of using functional near-infrared spectroscopy (fNIRS) data to classify trust levels in human-AI interaction scenarios. A total of 18 participants completed an image classification task with an AI team member while their hemodynamic responses were recorded using fNIRS. Preprocessing of fNIRS data involved motion artifact removal, filtering, and normalization. Exploratory analysis identified significant associations between hemodynamic responses in the prefrontal cortex and trust levels. An across-subject binary trust classification model was developed using machine learning techniques, achieving an F1 …

A Community-Based Study To Investigate Persistent Ambient Odors In Childs Park, Fl, Gennaro Saliceto

USF Tampa Graduate Theses and Dissertations

For many years, residents in Childs Park, Saint Petersburg, Florida, have reported unpleasant odors. Although there have been prior efforts to address the issue, the odors continue to be a concern for the community, who worry about possible health implications. This research aimed to equip residents with information to address the issue by identifying the presence of airborne pollutants that generate odors, quantifying the concentrations of toxic organic pollutants, and exploring the sources and environmental conditions that contribute to odors and their perception.

To accomplish these aims, the study utilized different methods. First, two walking measurement campaigns were conducted to …

Proceedings Of The Cuny Games Conference 11.0, Robert O. Duncan, Grace Axler-Diperte, Joe Bisz, Christina Boyle, Devorah Kletenik, Carolyn Stallard

Publications and Research

The CUNY Games Conference combines workshops, idea exchanges, interactive participant presentations, playtesting, and playing tabletop games into a one-day online event to promote and discuss game-based learning. The conference focuses on creative pedagogy, such as playful learning activities or games, that teachers can use in the classroom every day. The conference is online and features interactive presentations by attendees, informal idea exchange sessions, and workshops by the conference organizers.

Methodological Memorandum For Michelle Llado-Wrzos' Phd Dissertation, Michelle Llado-Wrzos Ed.D., Cristo Leon Ph.D.

Journal of Roleplaying Studies and STEAM

This methodological memorandum outlines the research approach for Michelle Llado-Wrzos' PhD dissertation, which explores the underrepresentation of women in Science, Technology, Engineering, and Mathematics (STEM) fields. Despite multiple efforts to reduce the gender disparity, female students remain significantly less likely to pursue STEM majors compared to their male peers. The research focuses on assessing the impact of extrinsic motivators—specifically financial incentives and exposure to female role models—on the STEM major choices of female high school students. By adopting a mixed-methods approach, this study thoroughly integrates quantitative surveys and qualitative interviews to examine these motivators.

A quasi-experimental design will assess the …

Cutting-Edge Methods For Analyzing Student Behavior In Educational Settings: A Review, Shatha Talib Rashid, Hasanen S. Abdullah

Journal of Soft Computing and Computer Applications

The ability to predict students' performance in educational settings like schools and universities is crucial. A key objective of this effort is to increase academic outcomes and prevent dropout rates, among other benefits. Automating student activities, encouraged by information collected from any technology-based learning tool, has an important role in the process here. Those big quantities of information ought to be completely studied theoretically and processed for gaining worthy insights concerning a student's background as well as interacting with scientific missions, facilitating the development of advanced ways and algorithms to predict students' performance. The current study reviews several contemporary mechanisms …

Modern Face Recgognition Systems: A Review Of Methods And Empirical Findings, Zahraa Naji Razoqi, Raheem Ogla, Abdul Monem S. Rahma

Journal of Soft Computing and Computer Applications

The face recognition system is a biometric technique that replaces traditional passwords and personal identification. This research is dedicated to presenting a study of some facial recognition systems. Since it is unlikely to replicate and is more stable over time, the domain of facial feature extraction has proven to be more effective in attaining exact facial recognition, which is important, especially in intelligent security surveillance systems. Face recognition systems encounter several challenges, primarily related to pose variations, illumination conditions, and occlusions such as hair, glasses, and so on. To address these challenges, enhance performance, and boost the accuracy and speed …

Evolutionary Dynamics Of Artificial Agents: Exploration And Learning In Games, Brian Mintz

Dartmouth College Ph.D Dissertations

The natural world abounds with examples of complex behavior in humans and many other species. Evolutionary game theory is a powerful mathematical framework to understand the origins of many such behaviors like cooperation. Since these behaviors are often selected against initially, understanding why they are so widespread has been a longstanding question. Rather than assuming agents' rationality, like in traditional game theory, this approach studies the mutation and selection of strategies themselves. However most behavior is neither perfectly rational nor entirely determined by genetics. This dissertation works to bridge the gap between these two perspectives by analyzing models where individuals …

Analyzing Human - Nonhuman Primate Conflict Mitigation Techniques In Mto Wa Mbu, Northern Tanzania, Lil Adams

Undergraduate Theses, Capstones, and Recitals

Human-wildlife conflict is a widespread challenge faced by those living in regular contact with wildlife that can have profound impacts on livelihood outcomes for humans, wildlife, and their shared environment (Barua et al., 2013; Blackie, 2023). Human – non-human primate conflict is particularly crucial due to primates’ high capacity to live among human populations (Chapman & Chapman, 1990; Alberts & Altmann, 2006; Reader et al., 2011; Sinha & Vijayakrishnan, 2017), and is currently on the rise due to increasing contact between human and non-human primates (Hockings, 2016; Uddin et al., 2020). To characterize and analyze techniques currently being used to …

Sentiment Analysis Of The Israel-Palestine Conflict On X: Insights From The Indonesian Perspective Using A Long Short-Term Memory Algorithm, Mohammad Taleb Noori, Muhammad Alif Rahman, Agus Purnomo, Aripin

Journal of Informatics and Web Engineering

The Israel-Palestine conflict which has persisted for decades drives mounting global interest that consequently influences public opinion worldwide. This article examines the sentiment analysis of X (Twitter) data pertaining to the conflict using the Long Short-Term Memory (LSTM) model. This study presents public reactions through an analysis of 1,700 tweets collected between May and July 2023 which encapsulate key recent developments. In this study, several steps were conducted, namely 1) crawling process to get raw data; 2) preprocessing: cleansing, case folding, tokenization, stop word removal, and stemming; 3) modelling and validation using the LSTM model; 4) model evaluation based on …

Sentiment Analysis And Topic Modelling On Twitter Related To Mobile Legends: Bang Bang Game Using Lexicon-Based, Lda, And Svm, Hikmal Muhammad, Fauzi Adi Rafrastara, Kristoforus Adrian Setiadi, Arley Japardi

Journal of Informatics and Web Engineering

Mobile Legends: Bang Bang (MLBB) has become a significant phenomenon within the global e-sports landscape, attracting millions of active players and fans. This study presents a comprehensive sentiment analysis and topic modelling of MLBB-related discussions on Platform Twitter, combining a lexicon-based approach, Latent Dirichlet Allocation (LDA), and Support Vector Machine (SVM) classification within a unified analytical pipeline. A dataset of 4,313 tweets was analysed, revealing that 70.8% expressed neutral sentiment, suggesting that much of the community's communication is informational rather than emotionally charged. Positive sentiments were associated with game content updates and rewards, while negative sentiments focused on technical and …

Diabetes Risk Prediction Using Shapley Additive Explanations For Feature Engineering, Chinwe Miracle Chituru, Sin-Ban Ho, Ian Chai

Journal of Informatics and Web Engineering

Diabetes is prevalent globally, expected to increase in the next few years. This includes people with different types of diabetes including type 1 diabetes and type 2 diabetes. There are several causes for the increase: dietary decisions and lack of exercise as the main ones. This global health challenge calls for effective prediction and early management of the disease. This research focuses on the decision tree algorithm utilization to predict the risk of diabetes and model interpretability with the integration of SHapley Additive exPlanations (SHAP) for feature engineering. Random forest and gradient boosting models were developed to identify the risk …

Machine Learning Model For Assessing Human Well-Being Using Brain Wave Activities, Sellappan Palaniappan, Rajasvaran Logeswaran, Yoke Leng Yong

Journal of Informatics and Web Engineering

This study presents a novel machine learning approach to assess human well-being through the analysis of brain wave activities. We developed a Random Forest classifier to categorize brain wave patterns into three states of well-being: good, normal, and bad. Using synthetic data simulating electroencephalography (EEG) readings, our model achieved an overall accuracy of 96.17%. The feature importance analysis revealed that alpha waves (34%) and beta waves (29%) were the most significant predictors of well-being states, which aligns with existing neuroscientific literature linking alpha activity to relaxation and beta activity to cognitive engagement. The confusion matrix demonstrated the model's particular strength …

Enhancing Multi-Step Stock Price Forecasting With Social Media Sentiment And Engagement Metrics, Damilare Olaniyan

Electronic Theses and Dissertations

This thesis investigates whether social media sentiment can improve the accuracy of stock price prediction beyond traditional historical data. While financial markets have long relied on structured numerical indicators, the growing influence of public discourse on platforms like Twitter has introduced new opportunities for extracting market-relevant signals from unstructured text. The study focuses on four major technology firms and combines sentiment features derived from Twitter with historical stock prices in a hybrid machine learning framework. Engagement-weighted sentiment, linguistic complexity, and polarity intensity were extracted using natural language processing techniques and incorporated into classification and regression models. Results show that including …

A Spatial Analysis Of Residential Photovoltaic Adoption In New Jersey: Focusing On 2020 With Trends Through 2025, Brandon Mcalister

Geography and the Environment: Graduate Student Capstones

This capstone project seeks to find the relationship between socioeconomic and demographic variables and their relationship to residential photovoltaic installations across New Jersey. The study consists of Geographic Weighted Regression Analyses to construct and socioeconomic index indicating the likelihood of photovoltaic adoption in New Jersey zip codes. Then using the index to predict future photovoltaic adoption across the state. The study found that socioeconomic and demographic variables can predict photovoltaic adoption within one year, but when placed in an index and used to predict future adoption trends, the model under predicts and extremely over predicts. While socioeconomic status is a …

Conceptualizing The Explanatory Fully Longitudinal Mixed Methods Case Study Design: A Demonstration With An Arithmetic Education Trial With Kindergarten Children, Menglong Cong

Electronic Theses and Dissertations

In employing longitudinal mixed methods designs, researchers have commonly used the fully longitudinal mixed methods design. This has occurred mostly in the health sciences but less in education. The current investigation proposes a novel longitudinal mixed methods research design, explanatory fully longitudinal mixed methods case study design. It demonstrates its potential for addressing research inquiries in educational research using the arithmetic learning trajectories datasets. This study is presented in three components. The first is a quantitative phase, selecting an exploratory case from a previous arithmetic learning trajectories study (Clements et al., 2021) for qualitative analyses based on maximizing the …

Simple Yet Effective, Effective Yet Inclusive: A Skincare Line, Ayesha Wali Rahimoon

Lawrence University Honors Projects

This honors project addresses the lack of inclusive skincare formulations by developing a scientifically grounded skincare line with an antioxidant-rich serum, moisturizer, and cleanser tailored to support diverse skin types, particularly melanin-rich skin. The serum combines strawberry extract powder, Manuka honey, niacinamide, green tea extract, and humectants to promote hydration, antioxidant protection, and skin barrier reinforcement. Formulated with a stable pH of 5.0–5.5 and an effective preservative system, the product was tested through non-animal, biologically relevant models.

A UV protection assay using HEK293 cells demonstrated that the serum significantly reduces oxidative damage, increasing post-exposure cell viability to 80%, compared to …

Cultivating Resilience In The Anthropocene: Evaluating A Gssw Ecological Justice Curriculum, Windy Selig

Electronic Theses and Dissertations

This Dissertation in Practice evaluated the University of Denver’s Graduate School of Social Work (GSSW) Ecological Justice Pathway to examine how resilience is conceptualized and cultivated within the curriculum. Grounded in a constructivist paradigm and guided by an ecological justice framework, this evaluation explored the pedagogical and curricular strategies designed to prepare social work students for the emotional, ethical, and professional demands of practicing in the Anthropocene—a time marked by ecological degradation, climate crisis, and compounding social inequities.

Through qualitative methods, including faculty interviews and curriculum document analysis, the evaluation identified both explicit and implicit components that foster student resilience. …

Data Sonification: The Art Of Exploring Space Through Sound, Jessica Cotturone

Music: Student Scholarship & Creative Works

Data sonification is a cutting-edge, interdisciplinary field that involves the transformation of data into sound. As the amount of data collected in today’s world continues to rise exponentially, new collaborations between scientists, mathematicians, and musicians are exploring the potential applications of sonification. Turning data into sound can create new avenues for interpretation and can increase accessibility for visually-impaired populations. The sonic renderings produced by this practice exist on a spectrum from sound to music and vary in their levels of objectivity and expressivity. One of the major areas where data sonification is providing a new scientific and artistic perspective is …

Virtual Reality’S Impact On Tourist Attitudes In Islamic Religious Tourism: Exploring Emotional Attachment And Vr Presence, Eman Alkhalifah, Ramy Hammady, Mahmoud Abdelrahman, Alyaa Darwish, Ella Cranmer, Ons Al-Shamaileh, Aikaterini Bourazeri, Timothy Jung

All Works

This study explores the integration of immersive technologies, specifically virtual reality (VR), to enhance tourist experiences in the rapidly expanding religious tourism sector. Despite VR’s potential, limited research has examined its impact on religious tourism. This study addresses this gap by investigating the role of emotional attachment in influencing VR presence during pre-, on-site, and postexperiences of VR-mediated religious tourism. A quantitative survey was conducted with 201 respondents who participated in VR religious tourism activities. The empirical analysis, conducted using SPSS and AMOS structural equation modeling (SEM), assessed how VR-mediated religious tourism impacts VR presence and tourist attitudes before actual …

Neat Vs. Scruffy: How Early Ai Researchers Classified Epistemic Cultures Of Knowledge Representation, Lindsay Poirier

Statistical and Data Sciences: Faculty Publications

Accounts of the history of symbolic artificial intelligence (AI) often categorize researchers and their programs through binary oppositions: logicist versus procedural, or alternatively, neat versus scruffy. These terms were often referenced throughout the 1970s and 1980s by AI researchers at institutions in the United States and Europe to document epistemic and esthetic differences in approaches to knowledge representation. In this article, I historicize and situate these binary oppositions, showing how researchers leveraged them in discourse to demarcate the beliefs and commitments underpinning certain AI approaches. I argue that the labels helped knowledge representation scholars identify, trouble, and grapple with the …

A Veblenian Approach To Chaos Theory, Isaiah Henry

Hatfield Graduate Journal of Public Affairs

This paper provides a comparative analysis of Veblen's belief in disequilibrium, the field of chaos theory, and how these two frameworks connect. We explore the similarities and differences between Veblen's theories and the principles of chaos theory, including their shared emphasis on complexity and unpredictability. By analyzing the connections between Veblen's economic theories and chaos theory, we can gain a deeper understanding of the nature of disequilibrium and the complex forces that shape our world.