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DNA Landscape Tool: An Interactive Webpage
No full textDeoxyribonucleic acid (DNA) is a molecule that carries genetic information that makes up every organism. DNA is typically introduced into the biology curriculum starting in high school and depending on an individual\u27s educational path can continue through college. Although taught frequently during school, elements of DNA can be difficult for students to grasp, due to the different visual representations of DNA found in various resources. For instance, when comparing an illustration of a chemically accurate nucleotide base to an abstract visualization of a chromosome learners often aren’t given enough scaffolding to see how one representation relates to the other, which can impede learning. This project set about to create an interactive webpage, that uses the DNA Landscape model, to illustrate and animate different levels of abstraction and scale of DNA. The DNA Landscape model consists of a nine-box grid with various visualizations of DNA. On the X-axis is scale (nucleotide bases to whole chromosomes) and on the Y-axis is abstraction (accurate to very abstract illustrations). Two adjacent boxes can be selected and subsequently, an animation demonstrates the relationship between the two illustrations. The animations clearly illustrate the transitions between visual representations of DNA to help learners understand how one image relates to the next. Additionally, each box has an information page that explains basic information for that illustration of DNA. The interactive function of the tool allows students to explore the concepts that they find most confusing. To help students recognize the importance of DNA’s structure, the interactive specifically uses the structure of the beta-globin gene for all the illustrations and animations. Overall, this project helps students understand how to visualize DNAs scale and structure and apply this knowledge to more complex concepts and processes that use DNA
Understanding Computer-Mediated Human Experience in Digital-Physical Hybrid Space
No full textWith the rapid development of mobile and immersive technologies, the boundary between the digital and real world is increasingly blurred, giving rise to hybrid spaces. Hybrid spaces have permeated daily life and reshaped spatial meaning-making and social interaction. Despite their growing prevalence, empirical understanding of human experiences in hybrid spaces remains limited. If left unexamined, hybrid spaces risk becoming technology-centered rather than human-centered environments that overlook socio-cultural dimensions, potentially leading to harmful consequences for individuals and society. To investigate human experience in hybrid spaces, this thesis comprises five studies and uses gameful systems as research probes. Prior literature identifies collaboration as a central theme in hybrid-spaces research; however, existing work provides limited empirical insight into the social dynamics of group collaboration in such settings. Studies 1 and 2 examine two collaboration configurations: (1) Mixed-Presence Collaboration (Study 1), integrating co-located and remote participants, and (2) Shared Augmented Reality (Study 2), where co-located users interact within a digitally layered environment. Alongside collaboration, the social ramifications of hybrid spaces—particularly the dynamics of trust—remain underexplored. Study 3 examines how people establish and manage trust in hybrid spaces, revealing how fairness, privacy, safety, and social acceptance are negotiated across intertwined digital and physical contexts. Building on the growing trend of public creativity and user-generated content, this thesis also explores the role of Generative AI in supporting interactive content creation by everyday users. Although interactive content is central to hybrid spaces, creating it remains technically demanding for non-expert users. Study 4 introduces DiaryPlay, an AI-assisted authoring system that enables users to express personal experiences by creating and sharing role-playing, interactive visual narratives. Finally, this thesis examines everyday information searching in hybrid spaces. As digital information layers overlapping physical surroundings become increasingly dense and recent advances in language models enable multimodal, conversational search, understanding remains limited regarding how people use these capabilities in practice, as well as their possibilities and limitations. Study 5 presents UrbanSearch, a technology probe that explores AI-powered, context-aware information searching within urban environments. Through contextual inquiry, I identify user behaviors and expectations, as well as limitations and potential risks, and derive implications for future design and development. In conclusion, this thesis contributes empirical insights, design implications, and novel interactive systems that advance our understanding of human experiences in hybrid spaces
The Usability and Influence of Comprehensive Sports Nutrition Handouts for Adolescent-Aged Female Athletes
No full textObjective: This study aimed to examine the usability and influence of comprehensive sports nutrition handouts for adolescent-aged female athletes. Design: Cross-sectional online survey incorporating post retrospective-pre self-assessment Participants: Adolescent female athletes between 13 and 17 years of age Methods: Participants received a series of digital nutrition handouts every day for seven days and completed an online survey. Variables: Age, sport, engagement, knowledge, behavior, features of interest, and experience with nutrition education. Analysis: Quantitative data were analyzed with descriptive statistics and qualitative data were examined using thematic analysis. A Wilcoxon signed-rank test assessed the change in responses for both knowledge and behavior. Results: Of the 71 participants who provided consent, 57 completed survey responses were eligible and included in the analysis. Participants were between 13 and 16 years old. Almost all participants (n=56, 98%) agreed or somewhat agreed that the handouts were helpful. Participants reported revisiting at least one handout (n=43, 75%), and 5–10 minutes was the most frequently reported time spent reading the handouts (n=31, 54%). Knowledge and intention to change behavior improved significantly (p\u3c 0.001). Participants reported that color was the feature that stood out the most (n=30, 54%), while increasing clarity/simplicity was the top suggested improvement (n=13, 25%). Conclusions: This study emphasized the importance of sports nutrition education for adolescent female athletes. Sports nutrition handouts for adolescent athletes appear to be a feasible way to share sports nutrition information, improve knowledge, and an athlete’s intention to change behavior
Selecting without Replacement from a Population of Bands of Serially Connected Objects
No full textThe sampling procedure from a finite population of objects that are serially attached into bands is described and analyzed. One object is randomly selected and removed at a time, which results in that object’s band being broken into two bands or shortened by one object. The main result gives the probability of choosing an object that is part of a band of serially connected objects of any specified size at each stage of the selection process
Enhancing Engineering Education for Students with Intellectual Disabilities through Universal Design for Learning
No full textThis study explores the impact of a Universal Design for Learning (UDL) aligned engineering curriculum on teacher confidence and student engagement in special education classrooms, specifically focusing on students with intellectual and developmental disabilities (IDD). The research investigates how the curriculum, combined with targeted professional development (PD), supports teachers in delivering high-quality engineering instruction. Through a mixed methods approach, including surveys, interviews, focus groups, and classroom observations, data revealed significant improvements in teacher self-efficacy, particularly in areas such as scaffolding, differentiation, and accessibility. Teachers reported increased confidence in their ability to implement engineering lessons and foster engineering mindsets in their students. Furthermore, the study highlights the effectiveness of UDL principles in promoting student collaboration, problem-solving, and independent learning. The findings emphasize the importance of data-informed PD, which not only enhances teacher practice but also provides equitable STEM learning opportunities for students with ID, contributing to the broader goal of advancing inclusive STEM education
Multipath Exploitation for Radar Sensing In An Urban Environment using The MUSIC Algorithm for Jammer Mitigation
No full textWith the increased push for spectrum sharing techniques, the technological world surrounding radar has been constantly pushing to find new methodologies. From being able to integrate with a communication system to mitigating the effects of other users on a system, the research community is pushing forward. We look to tackle the problem of mitigating a jammer using a multipath MUltiple SIgnal Classification (MUSIC) based approach. We take multiple steps to implement this and generate a framework for future work. We first take a look at understanding the current state of the art research surrounding DFRC, multipath exploitation, MUSIC algorithms, RIS and more. We then delve into the factors affecting MUSIC Algorithm performance, followed with a machine learning approach to classification of jammer presence. We then close by proposing a model to mitigate a jammer using the multipath in an urban environment. We show a near 99\% success rate in classification using a decision tree and also show a successful estimation of a jammers coordinate using multipath MUSIC and successful mitigation to uncover our original target. The ultimate purpose of this is to build a framework which will be built upon in the future for jammer mitigation
Optimizing Marketing Campaigns to Maximize the Response Rate for a Supermarket Using Machine Learning Techniques
No full textSupermarkets must improve their marketing tactics at a time of changing consumer behavior and increasing competition in the retail industry. Engaging the wide and sophisticated consumer base of today\u27s supermarkets is difficult using traditional methods. This proposal presents a data-driven approach using cutting-edge machine learning methods to enhance supermarket marketing strategies. The main goal is to increase the response rate of marketing initiatives, which will improve consumer engagement and ultimately increase revenue. Additional primary objectives are customer segmentation and targeting, predictive modeling, personalization, ongoing performance monitoring, and ROI evaluation. The current problem centers around personalization and accuracy. Due to the complexity of marketing channels and the demands of modern customers, flexible techniques are needed. The proposal offers a thorough process that includes data gathering, analysis, modeling using machine learning, personalization, and continual development. The idea attempts to create marketing efforts that connect with customers by using prediction models and recommendation systems. The results that are anticipated include a rise in supermarket sales and profitability as well as an improvement in consumer involvement and satisfaction. Additionally, this concept offers a chance to gather crucial information about consumer behavior for the next marketing initiatives. The strategy proposed in this proposal has the potential to change the marketing environment for supermarkets, enabling them to become more adaptable, receptive, and customer-focused in their operations. Predictive models are informed by the technique by utilizing a variety of data sources, including consumer profiles, purchasing history, demographics, and real-time information. Marketing campaigns may be customized due to the integration of regression, classification, and clustering algorithms in these models. Effective consumer segmentation and targeting guarantees that everyone receives customized advertisements and incentives, increasing response rates and engagement. It should be noted that Tableau will be used to extract visually appealing insights from the dataset, SPSS will be used to examine and prepare the data, and Kaggle will serve as the major source for data
Predicting Student Academic Performance Using Behavioural and Parental Engagement Data from Learning Management Systems
No full textThis paper explores how behavioral, academic, and parental engagement data provided within the xAPI-Edu-Data dataset can be used to predict the academic performance of students when training on machine learning models with supervised learning. Due to the developing demands of the data-driven initial selection of the learners under risk, the study will create a valid and explainable predictive model that can consider the most significant factors of student success in Learning Management System (LMS). The research is based on behavioral engagement and self-regulation learning theories; the observations included in the analysis of student interaction, i.e. resource usage, classroom engagement and engagement in discussions, in terms of their connexon to academic success. A positivist, quantitative approach was embraced with 480 student records with 16 variables of features in the category of demographic, academic, behavioral and parental involvement. Several models of supervised learning were tried and compared, among which were Random Forest, Gradient Boosting, Decision Tree, Naïve Bayes, Logistic Regression, Support Vector, and K-Nearest Neighbors. Preprocessing of data was done through categorical encoding, feature scaling where necessary and stratified train test splitting. Accuracy, precision, recall, F1-score and 10-fold cross-validation were used to assess the model performance. These findings show that the ensemble to be the most predictive in terms of accuracy (80.21 -\u3e the highest) were the ensemble with Random Forest and a voting classifier. The strongest predictors were found to be behavioral engagement variables, with limited roles being played by the demographic factors. The results prove the efficiency of ensemble learning in educational analytics and show that behavioral information is helpful in the early intervention and evidence-based educational decision-making