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Proceedings Of The Eleventh Annual Indiana Stem Education Conference: Stem Journeys, William S. Walker Iii, S. Selcen Guzey

Indiana STEM Education Conference

The Proceedings of the Eleventh Annual Indiana STEM Education Conference are edited by the Center for Advancing the Teaching and Learning of STEM (CATALYST, https://www.education.purdue.edu/catalyst/) at Purdue University. The theme for the 2026 conference is STEM Journeys, celebrating the multiple pathways and experiences that advance student learning in STEM education.

Renewable Energy Innovation: A Mathematics-Driven Integrated Stem Approach For K-12 Classrooms, Humphrey Chinenye Ochulor, Dayat Hidayat

Indiana STEM Education Conference

This research brief presents Renewable Energy Innovation (REI) as a mathematics-driven Integrated STEM (iSTEM) approach for engaging upper elementary and middle school students to design, analyze, test, and refine solar cars and wind turbines through mathematical modeling (MM) and problem solving (PS). Research suggests that renewable energy activities support students’ abilities to design systems, quantify variables, construct multiple representations, and refine mathematical models to clarify relationships between energy output and design features. Positioning mathematics as the driver in REI iSTEM engineering design contexts aligns with the Common Core State Standards for Mathematics and foregrounds mathematics as a primary organizing discipline …

Stem Integration Through Environmental Data Analysis For Real-World Problem Solving, Abigail Erskine

Indiana STEM Education Conference

Integrating authentic, environmental-based data can make STEM instruction more meaningful. As part of a broader high school STEM curriculum, I designed a lesson that connects mathematical and statistical concepts, technology, and environmental data through environmental data analysis to develop real-world problem solving. Using local deer mortality data, I examined how descriptive statistics and data interpretation could reveal population and ecological patterns. Designing with real data highlighted how mathematics can serve as a lens for understanding environmental issues and promote connections across STEM disciplines. This research brief focuses on the instructional design and reflection as the lesson designer.

The Influence Of Teachers And Career Counselors Of Middle School Students' Interest In Pursuing Stem Careers, Damilola Ayokanmi, Paul Asunda

Indiana STEM Education Conference

At the front of every STEM classroom, teachers hold more than just textbooks, they hold the potential to shape students’ aspirations, confidence, and perceptions of what is possible in science, technology, engineering, and mathematics (STEM). Beyond teaching content, they nurture problem-solving skills and position STEM careers as attainable (Dare et al., 2021). Career counselors complement this work by providing career-related information, exposing students to STEM professions, and guiding them through academic pathways (Rottinghaus et al., 2018). Together, these educators form an essential support system influencing students’ early career awareness (Murcia et al., 2020). This ongoing qualitative study examines how teachers …

Centering Learners' Voices: Mapping As A Tool For Visuospatial Reasoning In Mathematics, Maria Eloisa C. Nuguid

Indiana STEM Education Conference

Visuospatial reasoning considers visual imagery and the dynamics of space rooted in learners’ lived experiences. Mapping is an example of a visuospatial task where learners interact with and represent their environment. The research focuses on children’s experiences with mapping tasks in the context of informal science, technology, engineering, art, and mathematics (STEAM) education.

Machine Learning For Recession Prediction, Ethan Reusser

Williams Honors College, Honors Research Projects

Macroeconomic predictions present challenges in machine learning due to the rarity of economic recessions, the constantly-changing matter of global markets, and severe class imbalance in historical data. This project focuses on predicting the onset of United States economic recessions within a 12-month window using Python and Jupyter Notebook. A machine learning pipeline was developed utilizing multiple models: Logistic Regression, Random Forest, XGBoost, and Long Short-Term Memory (LSTM) neural networks. For class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied strictly to training data, paired with Platt scaling for calibration on thresholds. The resulting models were evaluated in the 2005 …

An Analysis Of Ohio's Advanced Quantitative Reasoning Course, Ethan A. Douglas

Williams Honors College, Honors Research Projects

Beginning in about 2018, The Ohio Department of Education and Workforce partnered with the Ohio Department of Higher Education and the Ohio Math Initiative to create a mathematics transition course to provide high school students with a class that could prepare them for a college entry-level mathematics course if they had not previously earned a remediation-free score. This course was titled “Mathematical Modeling and Reasoning” and has since been named, “Advanced Quantitative Reasoning” (AQR) and has become an Algebra II equivalent course where students can receive their final math credit to graduate. This study looked to compare students in Algebra …

Pipeline Corrosion And Relevant Microbial Influence, Renee E. Bamberger

Williams Honors College, Honors Research Projects

This study investigated the role of microbial activity and oxygen availability in the corrosion of carbon steel pipelines with a focus on microbially influenced corrosion (MIC). Sediment collected from Old Woman Creek, Ohio, was used to simulate natural soil conditions, and carbon steel coupons were incubated under four treatments: nonsterile oxic, nonsterile anoxic, autoclaved oxic, and autoclaved anoxic over 85 days. Corrosion was assessed through mass loss measurements and the analysis of pH, sulfate, chloride, and dissolved iron concentrations. The greatest mass loss was observed in autoclaved oxic conditions, contrary to the initial hypothesis that microbial activity would greatly enhance …

Designing Classroom Routines To Reduce Math Anxiety: A Growth Mindset And Productive Struggle Toolkit For Middle Schoolers, Heather Carey

Williams Honors College, Honors Research Projects

Math anxiety is an evident barrier to the success of students in middle school mathematics which often leads to decreased confidence, lower achievement overall, and avoidance of more challenging tasks. Research in mathematics education advocates that the instructional approaches that are more grounded in growth mindset and productive struggle can positively influence students’ attitudes toward learning and their engagement with solving more complex problems.

This project examines how researched-based strategies can be rendered into practical classroom applications. Using a qualitative design-based approach, this study integrates math anxiety, growth mindset, and productive struggle existing literature to inform the development of instructional …

Secure The Database: A Red Team, Blue Team Analysis Of Sql Injection, Andrew N. Miller

Williams Honors College, Honors Research Projects

SQL injection (SQLi) attacks are a type of cyberattack that seeks to bypass website logins and gain entry to sensitive information. These pose a significant danger to organizations holding confidential user information. Personally Identifiable Information (PII) like physical addresses, emails, phone numbers, social security numbers are at risk of theft. Login credentials like usernames, passwords, and other sensitive information like financial details and social security numbers are also exposed through SQLi attacks. SQLi attacks harm the confidentiality, integrity, and availability of people’s identity. Additionally, data breaches that reach public battention harm the reputation and trust of organizations. SQLi attacks rank …

Large Telescope Mount For Long Exposure Photos, Payne M. Landis, Anna J. Gray

Williams Honors College, Honors Research Projects

When viewing the stars through a telescope, it is common that the object you are viewing drifts out of view. Many amateur astronomers seek to capture long exposure images of the stars, planets, and galaxies with commercially available sky tracking telescope mounts. The complication is that these telescope mounts are both incredibly expensive and have low weight limits. These issues limit accessibility to these products based on the telescope. For each person, telescope parameters determine the amount of light and magnification that can be viewed, which then determines exposures time for adequate photo results. The options on the market for …

Optimization Of Post-Processing Methods For Additively Manufactured Metals, Julia R. Carano

Williams Honors College, Honors Research Projects

Ultrasonic Nanocrystal Surface Modification (UNSM) is a process used to change the surface hardness of flat-faced materials. It is a machining process using a high-powered laser and a blunt-tipped tool at high speeds which aims to improve the uniformity of the surface on most metals. By heating and pressing the surface of the workpiece, the grains of the material become less rounded and more cohesive on a microscopic level. This, ideally, results in a workpiece with improved material properties. The changes were previously observed through hardness testing.

Thermal Radiation Effects On Particles From Plasma Arc Radiation, Reece Davis, Joseph Mcgee

Williams Honors College, Honors Research Projects

Understanding the radiation heating and transport of both microparticles and nanoparticles is a phenomenon that is critical for both space and Earth sciences. In space, these particles are heated by plasma arcs within ionized space clouds or solar flares and can be present the path of deep space missions and orbital satellites. While on Earth, processes such as forest fires, nuclear fusion, and microchip manufacturing include the plasma arc heating of particles. These particles lay within the Mie Scattering Regime and Rayleigh Scattering Regime and emit radiation differently based on their diameter. New mathematical and computational modeling has been conducted …

Development Of A Signature Model For Predicting Normal Boiling Point Using Machine Learning, Mackenzie Karabin

Williams Honors College, Honors Research Projects

The goal of this project was to utilize the Signature molecular descriptor to construct a predictive model for computing the normal boiling point using a 4856-compound dataset. The final model intends to be applied to screening compounds whose experimental normal boiling point values are not readily available. Compounds were deconstructed into their height-1 atomic Signatures, resulting in 225 unique descriptors for the dataset, which were used as independent variables for creating the model. Machine learning (ML) techniques were employed to evaluate the applicability and accuracy of different ML models for predicting normal boiling point using the Regression Learner App in …

Computer-Aided Molecular Design To Identify More Environmentally Friendly Pfas, Melvin M. Keita

Williams Honors College, Honors Research Projects

The goal of this project is to inversely design alternatives to per- and polyfluoroalkyl substances (PFAS) using Computer-Aided Molecular Design (CAMD). PFAS, also described as “forever chemicals”, have been used in industry and consumer products since the 1940s. PFAS can be found in drinking water, food, food packaging, waste sites, and other sources. Exposure to different PFAS can lead to increased risks of some cancers, immune effects, and reproductive effects. Pulling from existing data, this project will use quantitative structure-property relationships (QSPRs) to design PFAS alternatives that possess optimal properties to prevent adsorption into drinking water and other potential sources …

C. Difficile Detection Method For First Responder Glove Application, Alli N. Senedak

Williams Honors College, Honors Research Projects

Clostridioides Difficile (C. diff) is a Anaerobic Gram-positive bacillus that is capable of spore formation, making it difficult to control its spread and duration in clinical environments. This phenomenon can provide danger to first responders, healthcare workers, and patients. The goal of this project is to create a biosensor capable of detecting C. diff in a clinical setting that can be applied to a glove apparatus. The project will involve the use of C. diff aptamers activated on the surface of an electrode. Once surface activation has been verified via surface analysis, the electrodes will be exposed to C. diff …

Predictive Structural Modeling In Ansys And Destructive Testing For Composite Structures, Dylan Matthews, Grant Abraham, Andrew Raineri

Williams Honors College, Honors Research Projects

Composite structures are central to the performance and reliability of Zips Racing’s Formula SAE vehicles, yet the team currently relies on historical data and partially unvalidated assumptions when selecting carbon fiber layups. This project aims to establish a quantitative, experimentally validated framework for designing composite components through the integration of simulation modeling, physical fabrication, and destructive mechanical testing. Analytical laminate theory models developed in MATLAB and laminate simulations created in Ansys ACP will be used to predict stiffness, strength, and failure characteristics for a range of candidate layups. These predictions will be validated through controlled testing of fabricated composite coupons …

Some Applications Of Derivatives In The Theory Of Equations, Commodore C. Dearman Jr

Electronic Theses and Dissertations

No abstract provided.

Fitting A Square Peg In A Round Hole – The Regulatory Landscape Of Cryptocurrency, Jaxon Hill

Minnesota Journal of Law, Science & Technology

No abstract provided.

Machine Learning For Economists, John Luke Gallup

Economics Faculty Publications and Presentations

Explication of machine learning algorithms and their usefulness for economic research. The prediction algorithms of Random Forest, Gradient Boost Machines, Neural Networks and Support Vector Machines are built from simple steps applied at large scale to generate surprisingly precise nonlinear estimates. Although useful for processing and interpreting new forms of data, their application to economics research is limited because they do not provide readily interpretable evidence of the causes of outcomes.

Transforming Florida Chemistry Education: Exploring Information Literacy Strategies From Singapore, Han N. Le

Honors Undergraduate Theses

This study explores the applicability of Singapore’s information literacy (IL) strategies within Central Floridian higher education environments. Singapore’s national IL framework, supported by its Ministry of Education, integrates information and communication technology (ICT), professional development, and lifelong learning initiatives through a standardized, systemwide approach. In contrast, IL instruction in the United States is largely decentralized, often limited to one-time sessions or elective courses. To investigate the feasibility of adapting Singapore’s practices, surveys were distributed to faculty and librarians across Florida’s twelve public universities, yielding responses that highlighted varying levels of IL integration, limited institutional support, and minimal professional training related …

A Systematic Literature Review Of The Possible Negative Impacts Of Overconsumption Of Mass Media And Technology On Elementary-Aged Members Of Generation Z (1997–2012) And Generation Alpha (2013–2024), Ashya Y. Warren

Honors Undergraduate Theses

This thesis explores the possible negative impacts of overconsumption of mass media and technology on the elementary-aged members of Generation Z (1997-2012) and Generation Alpha (2013-2024). This research is aimed at establishing the relationship between excessive and uncontrolled exposure to digital media and cognitive, emotional, social, behavioral, and academic difficulties at a critical period of child growth. The purpose of this systematic literature review can be explained by the author's experience with technology overuse and professional interests as a future teacher, who should be prepared to help promote healthy childhood and development. The systematic literature review was based on peer-reviewed …

Green Energy Innovation As A Theme For Integrated Stem Curriculum In K-8 Out-Of-School Learning, Humphrey Chinenye Ochulor, Paul Asunda, Swagata Sarkar, Comfort Temitope Aje, Deepanshu Gupta

Indiana STEM Education Conference

In this project, we developed a theme of Green Energy Innovation (GEI) for K-8 integrated STEM (iSTEM) out-of-school learning that includes scientific concepts, engineering design, mathematical measurement, and data collection. Anchored in engineering design (English, 2016), the researchers designed a curricular unit that included simple and complex solar car models. The researcher-designed solar car prototypes serve as STEM kits that support students’ iSTEM-based engineering design activities.

Exploring The Role Of Dialogue In Student Development And Knowledge Generation, Alison Warren

Indiana STEM Education Conference

In classrooms where students are given the space to engage in dialogue, ask questions, and negotiate ideas, science learning comes to life. Providing opportunities for students to negotiate ideas publicly, through dialogue, then privately, through writing has the potential to maximize student idea generation. Although dialogue is widely recognized as important for engagement and communicating ideas, it has also been framed as an epistemic tool. However, the role of dialogue as a sense-making process or epistemic tool, and its relationship to other forms of language, remains under-explored. This study addresses this gap by investigating the role of dialogue and how …

Indiana Bridges: Inclusive Stem Learning For Students With Down Syndrome, Elizabeth N. Christian, Jeffrey A. Nowak

Indiana STEM Education Conference

This research brief explores how materials and the engineering design process may be used to support inclusive STEM learning for students with Down syndrome. Conducted as a series of five consecutive workshops over a summer break, the Indiana Bridges project introduced engineering concepts through bridge-themed sessions created to be accessible for all ages and abilities. Students ages 11-20 with Down syndrome and two teacher education faculty participated in five workshops. Family members and center support staff frequently participated. Bridges from across the state of Indiana and classroom materials familiar to the participants provided entry points while new materials extended their …

More Than A Design Task: How Questions Shape Student Attention And Learning, Isaiah Kent-Schneider

Indiana STEM Education Conference

Incorporating engineering design activities into education has the potential to transform learning and students' educational experiences. However, when teachers implement engineering design without asking purposeful questions to focus students' attention, students tend to focus on the task but miss out on the intended learning of underlying science and math concepts. This research brief provides practical examples of questions teachers can use to focus student attention during engineering design activities.

Investigating The Interaction Of Gram-Positive Lactobacillus Species With Mucin In The Context Of Barrett's Esophagus, Ritisha Suresh

Honors Undergraduate Theses

Gastroesophageal Reflux Disease (GERD) can progress to the precancerous Barrett’s Esophagus (BE) condition, in which the normal esophageal squamous epithelium is replaced with a columnar epithelium containing goblet cells. Mucins, sticky substances, are secreted in response to bile acid exposure during reflux episodes to protect the esophageal epithelium. Goblet cells secrete these mucins, which can also scaffold microbial colonization. Probiotic Lactobacillus bacteria can use glycosidase enzymes to process mucin and metabolize them into their component sugars (glycans). This project aims to identify the role of the Lactobacillus-mucin interaction in BE, as glycan availability could confer an advantage to Lactobacillus …

Data And Code For: Combined Effects Of Forest Fire And Climate Warming On Seasonal Snow In The Western Oregon Cascades, Joseph D. Vigil

Environmental Science and Management Datasets

This dataset supports a study examining the combined effects of forest fire and climate warming on seasonal snow in the Breitenbush watershed of the western Oregon Cascades. The archive includes daily SnowModel snow water equivalent outputs for forested, current post-fire, mid-century, and end-century climate scenarios; field meteorological and manual snow observations; station metadata; modified SnowModel code used to represent post-fire snow-albedo decay; and R code used to calculate snow metrics and statistical comparisons reported in the associated manuscript. Derived results and documentation are included to support reproduction of the primary analyses.

Using Low-Cost Sensors For Source Attribution And Health Assessment: An Air Quality Study In Brownsville, Texas, Sai Deepak Pinakana, Kabir Bahadur Shah, Daniel Jaffe, Juan L. Gonzalez, Owen Temby, Gabriel Ibarra-Mejia, Amit U. Raysoni

School of Earth, Environmental, & Marine Sciences Faculty Publications

Air quality monitoring remains a challenge in areas lacking or having sparse federal monitoring infrastructure, posing significant barriers to public health research. This study demonstrates the usage of low-cost sensors in addressing gaps in air quality monitoring, source attribution, and health risk assessment in a Brownsville, TX neighborhood impacted by emissions from a barite and celestite mineral processing unit. PM2.5 concentrations were measured using PurpleAir sensors deployed across three residential locations, with the site nearest to the processing unit recording a 24-h averaged PM2.5 concentration of 25.12 μg/m3—approximately 2.79 times higher than the nearest Texas Commission of Environmental Quality (TCEQ) …

Proactive Safety Reasoning In Human-Robot Collaboration In Disassembly Through Llm-Augmented Stpa And Fmea, Morteza Jalali Alenjareghi, Fardin Ghorbani, Samira Keivanpour, Yuvin Adnarain Chinniah, Sabrina Jocelyn

Études primaires

Disassembly tasks in human–robot collaboration (HRC) environments present safety challenges due to hazardous materials, control system variability, and physically demanding operator tasks. To address these challenges, we propose an AI-augmented risk assessment framework integrating System-Theoretic Process Analysis (STPA) and Failure Mode and Effects Analysis (FMEA). This framework is implemented in four configurations: Term Frequency– Inverse Document Frequency (TF-IDF), Fine-tuned Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and RAG with a structured Knowledge Graph (KG) built from safety standards. The system supports real-time, standards-compliant safety reasoning by generating interpretable, context-specific recommendations. We evaluate these configurations across GPT-3.5 TURBO, GPT-4o, GPT-4.1, and …