Margie and Bill Klesse College of Engineering and Integrated Design Faculty Research

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12588/829

Browse

Now showing 1 - 20 of 263

Examining the Impact of Natural Ventilation versus Heat Recovery Ventilation Systems on Indoor Air Quality: A Tiny House Case Study
(MDPI, 2024-06-14) Karaiskos, Panos; Martinez-Molina, Antonio; Alamaniotis, Miltiadis
Adverse health effects can arise from indoor air pollutants, resulting in allergies, asthma, and other respiratory problems among occupants. Concurrently, the energy consumption of residential buildings, particularly concerning heating, ventilation, and air conditioning (HVAC) systems, significantly contributes to global energy usage. To address these intertwined challenges, heat recovery ventilation (HRV) has emerged as a viable solution to reduce heating and cooling demands while providing fresh ventilation rates. This study aims to investigate the indoor air quality (IAQ) of an experimental tiny house building equipped with an HRV unit by simulating real-life scenarios contributing to IAQ. The research evaluates the effectiveness of HRV compared to natural ventilation in managing particle matter (PM), total volatile organic compounds (TVOC), formaldehyde (CH₂O), carbon monoxide (CO), and carbon dioxide (CO₂) levels. This research significantly contributes to the understanding of the different ventilation strategies’ impact on IAQ in tiny houses and offers valuable insights for improving living conditions in a unique building typology that is underrepresented in the research literature.
Thermal Evaluation of the Initial Concept 3.X Vehicle at Mach 7
(MDPI, 2024-06-13) Dhanagopal, Abinayaa; Strasser, Nathan S.; Andrade, Angelina; Posladek, Kevin R.; Hoffman, Eugene N. A.; Combs, Christopher S.
High-speed global surface temperature distributions and heat flux measurements on the Initial Concept 3.X vehicle (IC3X) model were investigated at the UTSA Mach 7 wind tunnel, examining angles of attack of 0° and 5° at a freestream unit Reynolds number (Re) ~7 × 10⁶ m⁻¹. A ruthenium-based, fast-responding, temperature-sensitive paint (fast-TSP) prepared in-house was applied to a 7.1% scale model of the vehicle. Static calibration was performed to convert the intensity measurements into surface temperature values. The surface temperatures and derived heat flux fields conformed to the predicted trends, which was corroborated by Schlieren flow visualization. Notably, the average surface temperature variation was identified to range from 6 to 34 K at a 0° angle of attack and from 11 to 44 K at a 5° angle of attack, with the most pronounced gradient detected at the stagnation point. Additional measurements provided a detailed thermal assessment of the model, including estimations of the stagnation point heat flux, the convective heat transfer coefficient, and the modified Stanton number. Statistical and time series analyses of the data collected revealed the absence of prevailing unsteady phenomena, suggesting that the tested design geometry is well suited for hypersonic flight applications. These experimental outcomes not only shed light on the aerothermodynamics experienced during high-speed flight but also underscore the effectiveness of fast-TSP in capturing both quantitative and qualitative thermal data.
Designing Multifunctional Multiferroic Composites for Advanced Electronic Applications
(MDPI, 2024-06-09) Pereira, Lilian Nunes; Pastoril, Julio Cesar Agreira; Dias, Gustavo Sanguino; Santos, Ivair Aparecido dos; Guo, Ruyan; Bhalla, Amar S.; Cotica, Luiz Fernando
This paper presents a novel approach for the fabrication of magnetoelectric composites aimed at enhancing cross-coupling between electrical and magnetic phases for potential applications in intelligent sensors and electronic components. Unlike previous methodologies known for their complexity and expense, our method offers a simple and cost-effective assembly process conducted at room temperature, preserving the original properties of the components and avoiding undesired phases. The composites, composed of PZT fibers, cobalt (CoFe₂O₄), and a polymeric resin, demonstrate the uniform distribution of PZT-5A fibers within the cobalt matrix, as demonstrated by scanning electron microscopy. Detailed morphological analyses reveal the interface characteristics crucial for determining overall performance. Dielectric measurements indicate stable behaviors, particularly when PZT-5A fibers are properly poled, showcasing potential applications in sensors or medical devices. Furthermore, H-dependence studies illustrate strong magnetoelectric interactions, suggesting promising avenues for enhancing coupling efficiency. Overall, this study lays the basic work for future optimization of composite composition and exploration of its long-term stability, offering valuable insights into the potential applications of magnetoelectric composites in various technological domains.
Improving the Concrete Crack Detection Process via a Hybrid Visual Transformer Algorithm
(MDPI, 2024-05-20) Shahin, Mohammad; Chen, F. Frank; Maghanaki, Mazdak; Hosseinzadeh, Ali; Zand, Neda; Khodadadi Koodiani, Hamid
Inspections of concrete bridges across the United States represent a significant commitment of resources, given their biannual mandate for many structures. With a notable number of aging bridges, there is an imperative need to enhance the efficiency of these inspections. This study harnessed the power of computer vision to streamline the inspection process. Our experiment examined the efficacy of a state-of-the-art Visual Transformer (ViT) model combined with distinct image enhancement detector algorithms. We benchmarked against a deep learning Convolutional Neural Network (CNN) model. These models were applied to over 20,000 high-quality images from the Concrete Images for Classification dataset. Traditional crack detection methods often fall short due to their heavy reliance on time and resources. This research pioneers bridge inspection by integrating ViT with diverse image enhancement detectors, significantly improving concrete crack detection accuracy. Notably, a custom-built CNN achieves over 99% accuracy with substantially lower training time than ViT, making it an efficient solution for enhancing safety and resource conservation in infrastructure management. These advancements enhance safety by enabling reliable detection and timely maintenance, but they also align with Industry 4.0 objectives, automating manual inspections, reducing costs, and advancing technological integration in public infrastructure management.
The Dolan Fire of Central Coastal California: Burn Severity Estimates from Remote Sensing and Associations with Environmental Factors
(MDPI, 2024-05-10) Oseghae, Iyare; Bhaganagar, Kiran; Mestas-Nuñez, Alberto M.
In 2020, wildfires scarred over 4,000,000 hectares in the western United States, devastating urban populations and ecosystems alike. The significant impact that wildfires have on plants, animals, and human environments makes wildfire adaptation, management, and mitigation strategies a critical task. This study uses satellite imagery from Landsat to calculate burn severity and map the fire progression for the Dolan Fire of central Coastal California which occurred in August 2020. Several environmental factors, such as temperature, humidity, fuel type, topography, surface conditions, and wind velocity, are known to affect wildfire spread and burn severity. The aim of this study is the investigation of the relationship between these environmental factors, estimates of burn severity, and fire spread patterns. Burn severity is calculated and classified using the Difference in Normalized Burn Ratio (dNBR) before being displayed as a time series of maps. The Dolan Fire had a moderate severity burn with an average dNBR of 0.292. The ignition site location, when paired with the patterns of fire spread, is consistent with wind speed and direction data, suggesting fire movement to the southeast of the fire ignition site. Patterns of increased burn severity are compared with both topography (slope and aspect) and fuel type. Locations that were found to be more susceptible to high burn severity featured Long Needle Timber Litter and Mature Timber fuels, intermediate slope angles between 15 and 35°, and north- and east-facing slopes. This study has implications for the future predictive modeling of wildfires that may serve to develop wildfire mitigation strategies, manage climate change impacts, and protect human lives.
Computational Investigation of the Mechanical Response of a Bioinspired Nacre-like Nanocomposite under Three-Point Bending
(MDPI, 2024-05-07) Yang, Xingzi; Rumi, Md Jalal Uddin; Zeng, Xiaowei
Natural biological nanocomposites, like nacre, demonstrate extraordinary fracture toughness, surpassing their base materials, attributed to their intricate staggered hierarchical architectures integrating hard and soft phases. The enhancement of toughness in these composites is often linked to the crack-deflection mechanism. Leveraging the core design principles that enhance durability, resilience, and robustness in organic materials, this paper describes the use of computational modeling and simulation to perform a three-point bending test on a 3D staggered nanocomposite intentionally crafted to mimic the detailed microstructure of nacre. We adopted a previously proposed interfacial zone model that conceptualizes the "relatively soft" layer as an interface between the "hard" mineral tablets and the microstructure's interlayer spaces to examine how the microstructure and interface characteristics affect the mechanical responses and failure mechanisms. By comparing the model's predictions with experimental data on natural nacre, the simulations unveil the mechanisms of tablet separation through adjacent layer sliding and crack deflection across interfacial zones. This study offers a robust numerical method for investigating the fracture toughening mechanisms and damage evolution and contributes to a deeper understanding of the complex interplays within biomimetic materials.
Experiences in Delivering Online CS Teacher Professional Development
(Association for Computing Machinery, 2024-03-07) Wilde, Jina; Beltran, Emiliano; Zawatski, Michael J.; Fernandez, Amanda S.; Prasad, Priya V.; Yuen, Timothy T.
This paper describes our team's experience in designing and delivering the online teacher professional development (PD) program, Computer Science for San Antonio (CS4SA), aimed at empowering educators with computer science (CS) knowledge to increase Latinx participation in CS and STEM education within a large, urban predominantly Latinx school district in South Texas. This paper highlights the successes, challenges, and lessons learned while facilitating two cohorts of the CS PD through online platforms during the COVID-19 pandemic. As a result of this program, participants recognized the importance of integrating CS into their classroom and becoming advocates for the discipline at the high school level. Additionally, teachers, investigators, and other personnel learned important lessons for enhancing the program's impact through collaboration with district administrators and refinement of the online learning experience.
Combined Emission Economic Dispatch using Quantum-inspired Particle Swarm Optimization and its Variants
(SAGE Publications, 2024-03-19) Asif, Muhammad; Amin, Adil; Jamil, Umar; Mahmood, Anzar; Ahmed, Ubaid; Razzaq, Sohail; Mahdi, Fahad Parvez
The ever-increasing electricity demand, its dependency on fossil fuels, and the consequent environmental degradation are major concerns of this era. The worldwide domination of fossil fuels in bulk electricity generation is rapidly increasing the emissions of CO2 and other environmentally dangerous gases that are contributing to climate change. The economic and emission dispatch are two important problems in thermal power generation whose combination produces a complex highly constrained nonlinear optimization problem known as combined economic and emission dispatch. The optimization of combined economic and emission dispatch aims to allocate the generation of committed units to minimize fuel cost and emissions, simultaneously while honoring all equality and inequality constraints. Therefore, in this article, we investigate a solution of the combined economic and emission dispatch problem using quantum particle swarm optimization and its two modified versions, that is, enhanced quantum particle swarm optimization and quantum particle swarm optimization integrated with weighted mean personal best and adaptive local attractor. The enhanced quantum particle swarm optimization algorithm achieves particles’ diversification at early stages and shows good performance in local search at later stages. The quantum particle swarm optimization integrated with weighted mean personal best and adaptive local attractor boosts search performance of quantum particle swarm optimization and attains better global optimality. The suggested methods are employed to achieve solution for the combined economic and emission dispatch in four distinct systems, encompassing two scenarios with 6 units each, one with a 10-unit configuration, and another with an 11-unit setup. A comparative analysis with methodologies documented in existing literature reveals that the proposed approach outperforms others, demonstrating superior computational performance and robust efficiency.
Housing perceptions and code enforcement: An assessment of demolition orders using street view imagery and machine intelligence
(SAGE Publications, 2024-03-04) López Ochoa, Esteban A.; Zhai, Wei
The rapid growth of U.S. Sunbelt cities has intensified urban development pressures. Low-income housing demolitions are a result of such pressures as they are “low hanging fruit” for urban renewal, which can be further intensified by housing quality perceptions. By combining deep learning on Street View images (STV) with machine learning, we provide a model that accurately predicts demolition orders and allows assessing the heterogeneity of these predictions depending on the evaluator’s perceptions. Based on fast-growing San Antonio (TX) data, our results show that automated models can be useful to assess human perception biases of code enforcers.
Bridges Consumption Analysis for Oversize and Overweight Vehicles on Texas Roads
(SAGE Publications, 2024-03-28) Weissmann, Jose; Weissmann, Angela J.; Inoue, Danilo Keniti Nais; Prozzi, Jorge A.
Transportation infrastructure in Texas, U.S., particularly its extensive bridge network, plays a vital role in supporting economic growth and ensuring safe and efficient mobility. With over 57,000 bridges, including more than 22,000 culverts, Texas faces the challenge of maintaining its infrastructure while dealing with limited funding resources. This paper presents the findings of a study conducted for the Texas Department of Transportation that focused on assessing the costs associated with bridge consumption by oversize and overweight (OS/OW) vehicles. The study aimed to calculate bridge consumption costs per mile for different OS/OW categories, considering factors such as axle load, spacing, bridge design life, and vehicle miles traveled (VMT). These consumption costs were calculated by analyzing traffic data and bridge inventory ratings. The results showed that, although OS/OW vehicles are responsible for the majority of the bridge consumption costs per mile, their lower VMT, relative to regular commercial vehicles, mitigates their influence on total bridge consumption costs to less than 15% of the total. The findings of this study can contribute to the ongoing efforts of transportation engineers and policymakers in addressing funding deficits and developing strategies to sustain the state’s bridge infrastructure.
Context Modulation Enables Multi-tasking and Resource Efficiency in Liquid State Machines
(Association for Computing Machinery, 2023-08-28) Helfer, Peter; Teeter, Corinne; Hill, Aaron; Vineyard, Craig M.; Aimone, James B.; Kudithipudi, Dhireesha
Memory storage and retrieval are context-sensitive in both humans and animals; memories are more accurately retrieved in the context where they were acquired, and similar stimuli can elicit different responses in different contexts. Researchers have suggested that such effects may be underpinned by mechanisms that modulate the dynamics of neural circuits in a context-dependent fashion. Based on this idea, we design a mechanism for context-dependent modulation of a liquid state machine, a recurrent spiking artificial neural network. We find that context modulation enables a single network to multitask and requires fewer neurons than when several smaller networks are used to perform the tasks individually.
NEO: Neuron State Dependent Mechanisms for Efficient Continual Learning
(Association for Computing Machinery, 2023-04-12) Daram, Anurag; Kudithipudi, Dhireesha
Continual learning (sequential learning of tasks) is challenging for deep neural networks, mainly because of catastrophic forgetting, the tendency for accuracy on previously trained tasks to drop when new tasks are learned. Although several biologically-inspired techniques have been proposed for mitigating catastrophic forgetting, they typically require additional memory and/or computational overhead. Here, we propose a novel regularization approach that combines neuronal activation-based importance measurement with neuron state-dependent learning mechanisms to alleviate catastrophic forgetting in both task-aware and task-agnostic scenarios. We introduce a neuronal state-dependent mechanism driven by neuronal activity traces and selective learning rules, with storage requirements for regularization parameters that grow slower with network size - compared to schemes that calculate weight importance, whose storage grows quadratically. The proposed model, NEO, is able to achieve performance comparable to other state-of-the-art regularization based approaches to catastrophic forgetting, while operating with a reduced memory overhead.
An Stochastic Cold Food Supply Chain (CFSC) Design
(Association for Computing Machinery, 2023-05-09) Hernandez-Cuellar, David; Castillo-Villar, Krystel K.
Produce supply chains are a critical part of the food industry, as they are responsible for ensuring that fruits and vegetables are delivered to customers on time, at the right cost, and with the desired quality. Over the past few decades, researchers have been proposing the usage of Hub-and-Spoke networks as a modeling method to optimize large-scale food supply chains. Traditional optimization of supply chains involves identifying and eliminating inefficiencies, reducing lead times, improving inventory management, enhancing supplier relationships, and leveraging technology to improve visibility and control over the entire process. However, the majority of previous models are deterministic and fail to consider the implicit variability of crop yields and the consequence of climate change on the agricultural supply. The increasing amount of CO2 in the atmosphere, along with the shifts in temperatures and weather patterns, may influence harvests. Consequently, forcing distributors and consumers to look up for different suppliers in other areas to compensate for the fluctuations in the supply of produce. In this research, a stochastic Hub-and-spoke network model and an optimization algorithm are proposed to reduce transportation costs by finding an optimal production, distribution, and transportation network while considering climate variability and its impact on crop yield. A case study for an stochastic Cold Food Supply Chain (CFSC) considering several scenarios with different weather conditions is created using climate models and real soil data for the state of California. Strawberry is studied in this work, given that California is one of the major strawberry-producing states in the U.S. The preliminary results of the analysis indicate that weather scenarios showing more precipitation are more likely to increase crop yield, while scenarios with less precipitation yield lower amounts of fresh fruit.
A Neural Network Powered Solution Approach for Computationally Expensive Mixed Integer Programs for Bio Jet-fuel Supply Chain Network Design
(Association for Computing Machinery, 2023-05-09) Keith, Kolton; Castillo-Villar, Krystel K.; Alaeddini, Adel
Bio jet fuels derived from feedstock offer a sustainable alternative to meeting energy needs. Modeling supply chains that produce said fuels can lead to computationally prohibitive mixed integer linear programs (MILP) that consider optimal facility location and materials routing. The present work proposes an iterative machine learning-based hybrid solution procedure that transfers some of the responsibility of facility location to the learner. First, given a random selection of facility locations, a collection of model solutions is generated. Next, a neural network is fit to the collection of solutions, with facility locations being the input and total supply chain (SC) costs as the output. Then, the next set of locations is selected to optimize the predicted output of the neural network. Finally, the MILP optimization model is called to test the selected locations, and the results are fed back into the neural network and the process is repeated. Numerical experimentation demonstrates the proposed solution procedures yield near-optimal solutions with 0.10-0.18% increase in objective function value alongside a 40-65% reduction in computational time.
Evaluation and improvement of student learning experience in the post-COVID world: A lean six-sigma DMAIC study
(SAGE Publications, 2023-08-07) Chang, Mike C.; Faruqui, Syed Hasib Akhter; Alaeddini, Adel; Wan, Hung-Da
In 2020, the COVID-19 pandemic necessitated a shift to remote work-from-home (WFH) setups, including in the education sector. This transition had a significant impact on the interaction between students and instructors. To address this, our study aims to investigate the effects of the sudden transition to online learning on teaching methodology and to propose improvements to enhance its quality. We have developed a scoring system to evaluate teaching quality in the post-COVID-19 world. The scoring function incorporates various metrics, including students’ performance, sentiment towards the course (course material, teaching method, communication, etc.), feedback scores for weekly lectures, and students’ retention scores for recorded/live lecture videos. Following the Lean Six Sigma (LSS) procedure (Define, Measure, Analyze, Improve, and Control—DMAIC), we assessed the overall quality of online courses. The undergraduate courses demonstrated an increase in overall score from 86.67% during the online transition to 90.0% after implementing the suggested improvements. For graduate courses, the initial face-to-face lecture score was 55.81%, which dropped to 50.28% during the first online transition. However, after a year, the score improved to 61.59%, indicating successful improvement efforts. Upon careful analysis of the data, this paper provides suggestions to enhance students’ online learning experience during situations similar to the COVID-19 pandemic. The outcomes of the study aim to improve the quality of online learning experiences for students.
Architectural Design Qualities of an Adolescent Psychiatric Hospital to Benefit Patients and Staff
(SAGE Publications, 2023-06-26) Norouzi, Neda; Martinez, Antonio; Rico, Zayra
Objectives: This study is focused on how architectural design of adolescent psychiatric hospitals could positively affect not only patients but also staff members working at the hospitals. Background: Adolescents between the ages of 12 and 18 are among the young population with the highest percentage of mental illness. However, there are limited number of intentionally designed psychiatric hospitals for adolescents. Staff who work in adolescent psychiatric hospitals may face workplace violence. Studies on environmental impacts suggest that the built environment affects patients’ well-being and safety as well as staff’s satisfaction, working condition, safety, and health. However, there are very few studies that focus on adolescent psychiatric hospitals and the impact of the built environment on both staff and patients. Methods: Data were collected through literature analysis and semi-structured interviews with staff of three psychiatric state hospitals with adolescent patient units. The triangulation of multiple data sources informed a set of environmental design conditions that captures the complexity and connectedness of architectural design and the occupants of an adolescent psychiatric hospital. Results: The results present architectural composition, atmosphere, lighting, natural environment, safety, and security as indispensable design conditions to create an enclosed and city-like campus that provides a serene, secure, and structured environment that benefit staff and adolescent patients. Conclusion: The specific design strategies that need to be incorporated in the architectural design of a safe and secure adolescent psychiatric hospital include an open floor plan that respects patients’ autonomy and offers privacy while always providing staff with full visibility of patients.
Gold Nanomaterial System That Enables Dual Photothermal and Chemotherapy for Breast Cancer
(2023-08-25) Wang, Lijun; Shrestha, Binita; Brey, Eric M.; Tang, Liang
This study involves the fabrication and characterization of a multifunctional therapeutic nanocomposite system, as well as an assessment of its in vitro efficacy for breast cancer treatment. The nanocomposite system combines gold nanorods (GNRs) and gold nanoclusters (GNCs) to enable a combination of photothermal therapy and doxorubicin-based chemotherapy. GNRs of various sizes but exhibiting similar absorbance spectra were synthesized and screened for photothermal efficiency. GNRs exhibiting the highest photothermal efficiency were selected for further experiments. GNCs were synthesized in bovine serum albumin (BSA) and integrated into citrate-capped GNRs using layer-by-layer assembly. Glutaraldehyde crosslinking with the lysine residues in BSA was employed to immobilize the GNCs onto the GNRs, forming a stable "soft gel-like" structure. This structure provided binding sites for doxorubicin through electrostatic interactions and enhanced the overall structural stability of the nanocomposite. Additionally, the presence of GNCs allowed the nanocomposite system to emit robust fluorescence in the range of ~520 nm to 700 nm for self-detection. Hyaluronic acid was functionalized on the exterior surface of the nanocomposite as a targeting moiety for CD44 to improve the cellular internalization and specificity for breast cancer cells. The developed nanocomposite system demonstrated good stability in vitro and exhibited a pH- and near-infrared-responsive drug release behavior. In vitro studies showed the efficient internalization of the nanocomposite system and reduced cellular viability following NIR irradiation in MDA-MB-231 breast cancer cells. Together, these results highlight the potential of this nanocomposite system for targeted breast cancer therapy.
Measuring the Impact of Unique Entry-Level Instructional Course Modules Designed to Inspire Computer Science Interest
(American Society for Engineering Education, 2016-06-26) Martinez Ortiz, Araceli; Guirguis, Mina
Recent research regarding university student perceptions of “Computer Science” as a field of study and their motivation to pursue such studies as a career opportunity reveal student misconceptions and lack of motivation. Many students report that they regard the study of computer science as narrowly equivalent to “programming”. Moreover, many are not consistently provided the opportunity to realize the true impact of the field within their entry-level courses since these early courses tend to focus on programming and syntax skill development. It is not until they are in their upper-level courses that they gain a broader understanding and by then, many of them have already left the field. It is hypothesized that this lack of clarity of the field at an early point in students’ academic career, coupled with the perception that the curriculum is largely irrelevant to their lives, has impacted the retention rates of computer science majors in the first two years of their academic study programs. This paper will report on a preliminary stage of a comprehensive project effort that aims to improve retention rates for computer science students in their entry-level courses through the development of course modules intended for inclusion in their entry-level curriculum. The theoretical basis for these modules will be reviewed and the design framework for the development of these models is discussed. The aim of these models is to highlight the difference between Computer Science and Programming, to show the relevance of Computer Science in recent advances in various fields, and to inspire students to appreciate Computer Science and the role of algorithms in our daily lives. The modules will cover various topics about the role of CS in cyber warfare, understanding biology, electronic voting, etc. In subsequent work, these modules will be launched as part of a mixed methods study to determine their effectiveness as compared to a control group not learning through these models and the impact of those modules on the retention rates of Computer Science majors.
Fifth Grade Students’ Understanding of Ratio and Proportion in an Engineering Robotics Program
(American Society for Engineering Education, 2011-06-26) Martinez Ortiz, Araceli
The research described in this study explores the impact of utilizing a LEGO-robotics integrated engineering and mathematics program to support fifth grade students’ learning of ratios and proportion in an extracurricular program. One of the research questions guiding this research study was “how do students’ test results compare for students learning ratio and proportion concepts within the LEGO‐robotics integrated engineering and mathematics program versus when using a non-engineering textbook‐based mathematics program?” A mixed method repeated measures experiment with a control group was conducted. The subjects were 30 fifth grade students from a large urban school district who participated in one of two intervention programs, a LEGO‐robotics integrated engineering and mathematics program (experimental) versus a non-engineering textbook-based mathematics program (control). The understanding of ratio and proportion through numerical computation was measured using the Intra‐Mathematical Proportional Reasoning Test (Intra‐Prop). The understanding of ratio and proportion in general‐context mathematical word problems was measured using the Extra‐Mathematical Proportional Reasoning Test in a General Context (Extra-Prop) and the understanding of ratio and proportion in a LEGO engineering context was measured using a mathematical tool called Extra-Mathematical Proportional Reasoning Test in an Engineering Context (Engin-Prop). Students’ understanding of select basic engineering and mathematics definitions was measured using the Background and Definitions Test (Definitions Test). Data collected included classroom video, student interviews and written mathematical assessments of ratio and proportion problems in the four forms defined above, using repeated measures across three time periods-- prior to the beginning of the intervention programs, after the conclusion of the intervention program and ten weeks after the conclusion of the intervention program. The results of this study indicated that all students were able to make significant progress in learning new concepts of ratio and proportion as a result of participating in the intervention program learning experiences. Experimental students’ performance on the Intra-Prop was not significantly higher than that of the control students’ performance. However, experimental students’ performance on the Extra-Prop, Engin-Prop, and Definitions tests was significantly higher than that of the control students, indicating that students that learn about ratio and proportion in an engineering related context improve in their understanding significantly and retain their learning for a longer period of time when they encounter these situations in an extra-mathematical context versus in an intra-mathematical context. In addition, and of special note to practitioners, is the fact that students in the experimental group were able to learn at least as much and as well (if not more) the mathematics content of ratio and proportion as compared to the control group of students, and in addition, within the same amount of time, control group students learned and retained engineering and related ratio and proportion mathematics concepts.
Development And Implementation Of Challenge Based Instruction In Statics And Dynamics
(American Society for Engineering Education, 2010-06-20) Freeman, Robert; Vasquez, Horacio; Knecht, Martin; Martin, Taylor; Fuentes, Arturo; Walker, Joan; Martinez Ortiz, Araceli
This paper discusses challenge-based instructional (CBI) materials developed for courses in Statics and Dynamics. This effort is a component of a funded College Cost Reduction and Access Act (CCRAA) grant from the Department of Education, and focuses on student retention and development of adaptive expertise. Studies have shown that minority science, technology, engineering, and math (STEM) students leave STEM undergraduate fields in part due to lack of real world connections to their classroom learning experiences. Furthermore, in STEM fields the conventional approach is to teach for efficiency first and for innovation only in the latter years of the curriculum. This focus on efficiency first can actually stifle attempts at innovation in later courses. Our response to these issues is to change the way we teach. CBI, a form of inquiry based learning, can be simply thought of as teaching backwards. In this approach, a challenge is presented first, and the supporting theory (required to solve the challenge) second. Our implementation of CBI is built around the How People Learn (HPL) framework for effective learning environments and is realized and anchored by the STAR Legacy Cycle, as developed and fostered by the VaNTH NSF ERC for Bioengineering Educational Technologies. The developed materials are a result of collaboration between faculty members at the University of Texas-Pan American (UTPA) and South Texas College (STC), a two year Hispanic Serving Institution (HSI).

Browse

Recent Submissions