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Humanizing curriculum history: Reflective and diffractive practices of teachers in South Korean education reform
No full textClick on the DOI link to access this article at the publishers website (may not be free).This paper seeks to humanize the history of curriculum reform by exploring the diverse relationships that teachers form with the national curriculum system in South Korea. Drawing on the concepts of reflective and diffractive practices, we analyze the professional trajectories of two teachers across three decades of national curriculum changes. One teacher’s professional career reflects a commitment to aligning teaching methods with curriculum reforms, while the other teacher considers teaching as a political act, emphasizing a teacher’s interpretive and agential roles to challenge the rigid boundaries between policy and practice. By juxtaposing these narratives, this study complicates traditional views of curriculum history, highlighting how teachers exercise critical agency in shaping their relationships with the national curriculum system. In documenting these entangled relationships, we advocate for a more inclusive understanding of teacher development that acknowledges marginalized voices and experiences often excluded from official curriculum history. This study underscores the importance of valuing the diverse ways that teachers interact with the curriculum system, arguing for a shift from a policy-driven perspective to one that embraces the dynamic interplay between curriculum structures and teacher agency. © 2025 Philosophy of Education Society of Australasia
Teaching dynamics of mechanical systems using a practical project: The dragster car challenge
No full textClick on the DOI link to access this article at the publishers website (may not be free).The objective of this study is to present a simple, practical and comprehensive project to teach dynamics of mechanical systems in undergraduate level engineering courses. For that purpose, the dragster car challenge is utilized as a practical application example, for which a pedagogical activity where the students, organized in groups of five and supervised by tutor, design, model and analyze the dynamic performance of a minicar propelled by a tension helical spring. The dragster car challenge engages students to quite complete project, since it includes a good number of aspects related to teaching dynamics, namely the elaboration of free body diagram, application of Newton’s laws of motion, resolution of the equations of motion, as well as the identification of the dominant and secondary variables related to the performance of the car. Aspects associated with the machining and building of physical prototypes of the dragster car are not focused in this study. This work deals with the formulation and resolution of the equations of motion of the dragster car, including dissipative effects, such as air drag resistance, friction existent between the axle and bearings and between the wheels and ground. The obtained solutions are implemented in computer codes, which allow students to analyze the dynamic response of the car in terms of its performance. This study demonstrates that the proposed challenge approach is effective in teaching dynamics of mechanical systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025
Predicting performance of heterogeneous edge-cloud systems using machine learning models
No full textA project presented to the Department of Electrical and Computer Engineering and the faculty of the Graduate School of of Wichita State University in partial fulfillment of the requirements for the degree of Master of Science.Edge-cloud systems are heterogeneous computational infrastructures designed to manage workloads across distributed environments. Accurate performance predictions of these systems are essential for optimizing resource allocation, reducing latency, and improving overall efficiency. Traditional performance analysis tools, which do not leverage machine learning (ML), often introduce significant errors. This study proposes the use of ML models to predict key performance metrics in heterogeneous edge-cloud systems. Five different ML models are trained and used to estimate the system’s performance. The models include Random Forest (RF), Long Short-Term Memory (LSTM), Deep Neural Network (DNN), Recurrent Neural network (RNN), and a hybrid model combining RNN and DNN. The input datasets for training are sourced from the Wichita State University Computer Architecture and Parallel Programming Laboratory (CAPPLab). The performance of these models is evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The predicted communication latency and power consumption values are then compared against previously generated VisualSim results to assess the accuracy and effectiveness of the ML-based approach. The simulation results indicate that the RF model delivers superior performance, as reflected in its lower testing MAE and RMSE, demonstrating its effectiveness in capturing system dynamics. The deep learning models—LSTM, DNN, and RNN—exhibit varying levels of accuracy, with some models outperforming others in specific scenarios. The hybrid RNN-DNN model strikes a balance between computational complexity and predictive accuracy. The ML-predicted values follow a similar trend to the VisualSim results
Psychometric properties of the self-as-context scale with a university counseling center sample
No full textThis is an open access article under the CC BY license.The model upon which acceptance and commitment therapy is based posits that its outcomes are mediated by increased psychological flexibility as a core process. Of the six subprocesses contributing to psychological flexibility, self-as-context has been investigated the least due to a lack of adequate assessment. An evaluation of the psychometric properties of at least one such measure—the Self-as-Context Scale (SACS)—has been primarily limited to nonclinical populations. To address this omission, we administered the SACS to students (N = 132) seeking psychological services from their university counseling center. A confirmatory factor analysis failed to find an adequate fit for a previously reported two-dimensional model of the SACS, suggesting that only total scores may be appropriate in research and practice involving clinical samples. All 10 items satisfactorily loaded on a single factor to produce reliable total scaled scores, which were, as expected, significantly lower for our participants than those from a general college student sample. Even lower scores were obtained for outpatients of a psychology training clinic compared to our sample, which provided additional support for the known-groups validity of the SACS. The limitations of the findings and implications for further investigations of the measure’s psychometric and functional properties are discussed. © 2025 by the authors
Wichita State Research and Innovation News, April 2025
No full textGame-changing Shocker Fly Lab project kicks off with $1 million lead gift -- Wichita State biology professor elected as prestigious AAAS Fellow -- Wichita State undergraduates recognized at K-INBRE Symposium -- (Featured student research): Lillie Nightingale, dancing with wildebeests -- Sejun Moon, exploring Mars -- Daniel Reichart, exploring the cosmos -- Anna Brake, bridging health care gaps -- Wichita Biomedical Campus facade comes into view as construction continues -- (Forward Together podcast with President Rick Muma): Episode 31: Pierre Harter, research and industry on campus -- Episode 33: Wichita Biomedical Campus -- Wichita State leads the way in microcredential accountability -- Grant will help Suspenders4Hope fight substance use disorder -- WSU in the news -- Research at Wichita State -- Innovation at Wichita Stat
Agent-based simulation for Kansas EV infrastructure resilience
No full textPresented to the 21st Annual Symposium on Graduate Research and Scholarly Projects (GRASP) held at the Rhatigan Student Center, Wichita State University, April 11, 2025.Research completed in the Department of Industrial, Systems, and Manufacturing Engineering, College of Engineering.INTRODUCTION: Electric vehicles are projected to comprise over 50 percent of vehicles on the road by the year 2050. However, the state of Kansas does not currently have the infrastructure (such as EV charging stations) to support this rapid increase. Defining optimal quantity and placement of high-powered charging stations ensures successful and safe statewide EV transportation at these rates. Additionally, EV infrastructure must prove to be resilient against extreme scenarios such as natural disasters that result in high charging demand due to mass evacuations.
PURPOSE: The goal of the study is to assess the resilience of current charging infrastructure along Kansas Interstate 70 against both typical and extreme scenarios and develop performance metrics such as charging station utilization, user range anxiety, Tesla versus non-Tesla performance, and expected impact of station shutdown or road closures.
METHODS: Research is conducted by developing an agent-based simulation using the modeling platform NetLogo. EV trips are generated from a Least Squares Optimization using traffic flow data in the state of Kansas. The simulation uses a road path mimicking major Kansas highways and interstates. It establishes a network of cities and “DC Fast” charging stations representing potential routes and stations available for statewide transportation purposes. By assigning agent or demographic-related behaviors to drivers as well as assigning variables (such as utilization rate or downtime due to maintenance) to respective charging stations, EV transportation is simulated across the state including necessary or preferred stops for charging.
RESULTS: This model allows the collection of various resilience performance metrics such as charging station utilization, waiting time, and charging queue length in response to the modification of input parameters including availability of specific charging stations, influx in traffic flow, or agent behaviors.
CONCLUSION: This model allows the identification of any deficiencies in the current EV infrastructure and can provide supportive analysis of solutions such as mobile charging units or information campaigns.Graduate School, Academic Affairs, University Librarie
Advanced techniques for electricity consumption prediction in buildings using comparative correlation analysis, data normalization, and Long Short-Term Memory (LSTM) networks: A case study of a U.S. commercial building
No full textThis is an open access article under the CC BY license.This study introduces innovative assessment techniques to comprehend the effect of six data normalization methods, implemented through the LSTM algorithm, on predicting electricity consumption in commercial buildings. The focus lies on analyzing the relationship between various normalization process and its integration with LSTM method concerning building electricity consumption. The LSTM model incorporates input nodes from diverse sources, including weather data, plug load data, and occupancy ratio. Six distinct normalization process—Min-Max, Mean, Z-score, Gaussian, VSS, and IQR—are applied to assess the model's evaluation on both training and test datasets. The study found that combining the LSTM method with Min-Max and IQR achieves lower figures, representing better performance and greater stability in comparison to alternative normalization techniques. These results described the critical role of data normalization in improving the performance of LSTM models, highlighting the importance of choosing suitable normalization techniques for specific applications while balancing improved accuracy against computational complexity. Furthermore, the study explores the correlation impact of variations in average input elements, particularly with a 5% increase and decrease in value, on electricity consumption in commercial buildings across different seasons. Plug loads emerge as dominant contributors to electricity consumption across various normalization methods and raw data, with temperature and humidity ratio exerting notable influence in specific seasons. © 2025 The Author
An assessment of prior choices in a hierarchical Bayesian model for failure data
No full textPoster and abstract presented at the FYRE in STEM Showcase, 2025.Research project completed at the Department of Mathematics, Statistics and Physics.In recent decades, reliability analysis has become increasingly important for risk assessment and management in industrial system control. Traditional statistical methods may fall short when the failure data is limited. Meanwhile, Bayesian inference offers a strong alternative by enabling the integration of prior knowledge, expert judgment, and historical data from similar systems to improve failure modeling and estimation. The hierarchical Bayesian modeling (HBM) framework explores how prior choices influence failure predictions. A beta-binomial likelihood is coupled with five distinct prior distributions to characterize the behavior of the industrial component in three data scenarios of varying sample sizes, reflecting real-world uncertainty and variability. The results demonstrate that in the presence of limited data, the prior selection significantly impacts posterior predictions, showing the sensitivity of Bayesian models to prior assumptions. The importance of careful prior selection to improve reliability estimates and support maintenance engineers in making more informed decisions under different process uncertainty
The DUNE muon spectrometer: A magnetic systems review
No full textThesis (M.S.)-- Wichita State University, College of Liberal Arts and Sciences, Dept. of Mathematics, Statistics, and PhysicsThe magnetic system of the Muon Spectrometer in the Near Detector hall of the Deep Underground Neutrino Experiment (DUNE) will enable energy measurements of beam interactions. Confirming the magnetic field within the steel plates of the detector is difficult and providing a measurement of the field to confirm the simulated field provides confidence in its operation. A pickup coil wrapped around individual plate segments would provide enough measurement to confirm computer models of the magnetic field within the plates of the detector
Department of Dental Hygiene Class of 2003
No full textFirst row (left to right): Kelly Anderson, RDH, MHS, Assistant Professor; Barbara Gonzales, RDH, MHS, Assistant Professor; Lourdes Vazquez, RHD, MS; Dr. Scott W. Wiggins, DDS, Supervising Dentist; Dr. Ron Neugent, DDS, Supervising Dentist; Denise Maseman, RDH, MS, Assistant Professor; Chairperson; Diane E. Huntley, RDH, PhD, Associate Professor; Pamela Bumpur, RDH, MHS, Clinical EducatorSecond row (left to right): Tara Robert, Class President/Rep 02-03; Emily Wallis, Class Vice President/Rep 02-03; Sally Rojas, SADHA President 02-03; Sandy Blair, SADHA Vice President 01-02Third row (left to right): Jenny Ostrom, Class Liasion; Amanda Rutledge, Class Secretary/Treasurer; Kylie Bruckner, SADHA Secretary 01-03; Kelly Bowden, SADHA Treasurer 02-03; Nikki AskrenFourth row (left to right): Emily Aukes-Janoscrat, Nicole Boe, Theresa Boster, Stephanie Boyce, Tracy Byram, Lee Dulac, Jessics Fox, Ginger Goetz, Nicole Lytle, Kara McConaughyFifth row (left to right): Kim Ngan Nguyen, Tony Nguyen, Erika Noska, Lindsey Orr, Melissa Paxson, Sam Pohlman, Tamar Teschke, Carrie VanPelt, Christy Winger, Kellie YarmerDigitized by University Libraries' Technical Services Institutional Repository & Digitization group.Personal and non-profit use only