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'Simple Mistakes' in College Algebra: an Analysis of Students' Perceptions of Their Errors Using Attribution Theory

International Journal of Research in Undergraduate mathematics Education 2025年 1-28页

作者： Ryals, Megan Hill-Lindsay, Sloan Pilgrim, Mary E. Burks, Linda C. Center for Applied Mathematics University of Virginia 156 Engineer’s Way Charlottesville 22904 VA United States Department of Math and Statistics San Diego State University 5500 Campanile Dr San Diego 92182 CA United States Department of Mathematics and Computer Science Santa Clara University 500 El Camino Real Santa Clara 95053 CA United States

Analyzing their mistakes affords students opportunities for improved learning and success in problem solving disciplines, especially when students are able to identify reasons for their errors and modify their future study behavior. To help students make appropriate adjustments, instructors must understand the language students use to describe their own academic performance. This study explores what students mean when they say they made “simple mistakes.” Eight students participated in interviews where they reviewed errors from a recent exam, explaining why errors were simple or not simple. We analyzed the interview data using Causal Attribution Theory and discovered how students’ attributions of their errors influence their classification of their errors as simple or not simple. Results include descriptions of simple and not simple distinctions for three categories of students and evidence of similarities among students within each category regarding their self-efficacy and persistence through struggle. The analysis also reveals how students plan to adjust their study approach based on their classification of their errors. Across categories, students believe simple mistakes are preventable through repetitive practice or memorization, while not simple mistakes require help seeking, but students within different categories differ in how they use these strategies. The results show that that different students can use the same language to convey very different meanings for their own error categorizations and that there is need for instructors to probe these meanings before providing guidance. Our results also provide support for previous calls for interventions aimed at retraining students’ causal attribution styles towards controllable, unstable causes. © The Author(s) 2025.

关键词： Attribution theory College Algebra Motivation Student errors

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Polyp image segmentation based on improved planet optimization algorithm using reptile search algorithm

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Neural Computing and Applications 2025年第8期37卷 6327-6349页

作者： Abd Elaziz, Mohamed Al-qaness, Mohammed A. A. Al-Betar, Mohammed Azmi Ewees, Ahmed A. Department of Mathematics Faculty of Science Zagazig University Zagazig Egypt College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China Faculty of Computer Science and Engineering Galala University Suze435611 Egypt Ajman University Ajman346 United Arab Emirates College of Computing and Information Technology University of Bisha Bisha Saudi Arabia Department of Computer Damietta University Damietta Egypt Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan

To recognize the potential for colon polyps to develop into cancer over time, early diagnosis is crucial for preventative healthcare. Timely identification significantly improves the prognosis and treatment outcomes for colorectal cancer patients. Image segmentation is crucial in medical image analysis for accurate diagnosis and treatment planning. Therefore, in this study, we present an alternative multilevel thresholding polyp segmentation method (MPOA) to enhance the segmentation of polyp images. The proposed method is based on enhancing the planet optimization algorithm (POA) by integrating operators from the reptile search algorithm (RSA). The evaluation of the developed MPOA is tested with different polyp images and compared with other image segmentation approaches. The results highlight the superior capability of MPOA, as evidenced by various performance measures in effectively segmenting polyp images. Furthermore, metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and fitness values demonstrate that MPOA outperforms the basic version of POA and other methods. The evaluation outcomes underscore the significant impact of RSA in enhancing the performance of POA for the segmentation of polyp images. © The Author(s) 2025.

关键词： Diseases

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Micro-Macro Decomposition of Particle Swarm Optimization Methods

arXiv

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arXiv 2025年

作者： Herty, Michael Veneruso, Sara RWTH Aachen University Institute of Geometry and Applied Mathematics Aachen Germany University of Ferrara Department of Mathematics and Computer Science Ferrara Italy

Solving non-convex minimization problems using multi-particle metaheuristic derivative-free optimization methods is still active area of research. Popular methods are Particle Swarm Optimization (PSO) methods, that iteratively update a population of particles according to dynamics inspired by social interactions between individuals. We present a modification to include constrained minimization problems using exact penalization. Additionally, we utilize the hierarchical structure of PSO to introduce a micro-macro decomposition of the algorithm. The probability density of particles is written as a convex combination of microscopic and macroscopic contributions, and both parts are propagated separately. The decomposition is dynamically updated based on heuristic considerations. Numerical examples compare the results obtained using the algorithm in the microscopic scale, in the macroscopic scale, and, using the new micro-macro *** Codes 35Q70, 35Q84, 35Q93, 49M41, 90C56 Copyright © 2025, The Authors. All rights reserved.

关键词： Particle swarm optimization (PSO)

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Anisotropic quark stars f（R,Lm,T） in gravity

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Chinese Physics C 2025年第2期 270-281页

作者： Takol Tangphati ?zzet Sakall Ayan Banerjee Anirudh Pradhan School of Science Walailak University Research Center for Theoretical Simulation and Applied Research in Bioscience and Sensing Walailak University Physics Department Eastern Mediterranean University Atrophysics Research Centre School of MathematicsStatistics and Computer ScienceUniversity of Kwa Zulu–Natal Centre for Cosmology Astrophysics and Space ScienceGLA University

We investigated the impact of f（R,Lm,T） gravity on the internal structure of compact stars,expecting this theory to manifest prominently in the high-density cores of such *** considered the algebraic function f（R,Lm,T）=R+αTLm,where a represents the matter-geometry coupling *** specifically chose the matter Lagrangian density Lm=-ρ to explore compact stars with anisotropic *** this end,we employed the MIT bag model as an equation of ***,we numerically solved the hydrostatic equilibrium equations to obtain mass-radius relations for quark stars（QSs）,examining static stability criteria,adiabatic index,and speed of ***,we used recent astrophysical data to constrain the coupling parameter a,which may lead to either larger or smaller masses for QSs,compared to their counterparts in general relativity.

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Analysis of Viral and Non-viral Categories of Video Through Social Media Using Machine Learning Techniques 15th

Analysis of Viral and Non-viral Categories of Video Through ...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Nagashree, B.A. Satheesh Kumar, S. Muthukumaran, V. Joseph, P. Rose Bindu Amrita Vishwa Vidyapeetham Mysuru Campus Mysuru570026 India Department of Computer Science School of Applied Sciences REVA University Bangalore India Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Mathematics Dayananda Sagar College of Engineering Bangalore India

ISBN: (纸本)9783031889912

People frequently use technological means to collect information from many sources, share it with others on social media, and communicate with friends on these platforms. Social media is therefore a crucial new tool for communication. Collaborative wikis, blogs, and content communities are just a few of the tools and technologies that make up social media. YouTube is the most widely used of these. The influencer’s content gets visible to the users of such platforms. The expansion of social media use has enabled massive and rapid spread ability of content, often called virality. This paper aims to examine the virality of different categories of YouTube videos using machine learning technique, where it plays a major role in analysis of YouTube data such as likes, dislikes, comment count and views. YouTube can serve as a path to virality and fabricate online videos, hence finding the correlation analysis among the videos, which enables us to classify the viral and non-viral video categories, thereby analysing the linear relationship among the features (likes, dislike, views and comment count. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Tweets

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Optimizing Fog Computing Performance: A Machine Learning-Based Task Scheduling Approach Using Fuzzy Logic and Clustering 1

Optimizing Fog Computing Performance: A Machine Learning-Bas...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Dbritto, Ceres Lopes, Joel Oli, Sanjay Joshi, Maulik S Singh, Aniket Rayal, Ashish St Louis United States Redmond United States Dayananda Sagar College of Engineering Department of Mathematics Bangalore560078 India Gujarat Power Engineering and Research Institute GTU Department of Humanity and Science Gujarat India Veer Madho Singh Bhandari Uttarakhand Technical University Department of Computer Science and Engineering Pithoragarh Campus India School of Applied and Life Sciences Uttaranchal University Department of Mathematics Dehradun248007 India

ISBN: (纸本)9798331518578

The ability to deliver results promptly has become crucial for analytical frameworks due to the swift increase in data volumes. Complex algorithms are needed to schedule data processing activities on distributed compute clusters efficiently. Because it is impractical to create and fine-tune a scheduling policy for every task, current systems rely on basic, generic heuristics and neglect workload peculiarities. Effective task scheduling is essential for optimising the performance of fog computing systems. Fog nodes are dynamic, heterogeneous, and resource-constrained, and traditional task scheduling techniques, which were largely created for centralised cloud settings, frequently cannot accommodate these characteristics. We provide a cutting-edge machine learning (ML) based solution that adjusts job assignment to the evolving needs of the Fog environment in order to get beyond these restrictions. Fuzzy logic, a reliable unsupervised learning method, is used with the K-Means clustering algorithm in our method to effectively group. We have used Python to model and assess our suggested method. The simulation findings validate the effectiveness of our scheduling method, demonstrating notable improvements in response time, reduced network usage, and execution time reduction in comparison to other ML and non-ML based scheduling techniques available in the Python framework. © 2025 IEEE.

关键词： Fog computing

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Synthesizing 3D Axon Morphology: Springs are All We Need 15th

Synthesizing 3D Axon Morphology: Springs are All We Need

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Cui, Ruiqi Bærentzen, J. Andreas Dyrby, Tim B. Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens Lyngby2800 Denmark Danish Research Centre for Magnetic Resonance Centre for Functional and Diagnostic Imaging and Research Copenhagen University Hospital - Amager and Hvidovre Hvidovre2650 Denmark

ISBN: (纸本)9783031869198

The realism of digital phantoms for the white matter microstructure is highly valued. Realistic synthesis provides reliable input to generate synthetic diffusion MRI signals for evaluating biophysical models or training machine learning models of microstructure features, such as axon diameter, shapes, and cellular structures. Inspired by the popular spring-mass systems used in physical simulation, we propose a novel and flexible method for synthesizing axon morphology and its dynamics with physical constraints. Specifically, starting with an initial axon configuration, our method constructs a spring-mass system based on specific sampling rules inspired by the real 3D axons and cell morphology observed in X-ray synchrotron imaging. By minimizing the spring potential energy, our method optimizes the positions of sampled mass points, thereby deforming the axon morphology from its physical surroundings. After the optimization, a triangle mesh of the axon surfaces is obtained and can be used as input for Monte Carlo diffusion MRI simulations. Experimental results demonstrate that our approach successfully mimics a range of axon morphologies and the dynamic environment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image coding

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Integrative Analysis of Gene Co-Expression Networks Combined with Flux Balance Analysis of Metabolic Models for Insights into Chronic Obstructive Pulmonary Disease

Integrative Analysis of Gene Co-Expression Networks Combined...

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering (ECTI-NCON)

作者： Thummarat Paklao Apichat Suratanee Kitiporn Plaimas Department of Mathematics and Computer Science Faculty of Science Chulalongkorn University Bangkok Thailand Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

Chronic obstructive pulmonary disease (COPD) is a complex respiratory condition involving multifaceted molecular mechanisms. Differential gene expression analysis has identified genes with significant up-regulation and down-regulation in COPD patients compared to healthy controls, as well as those exhibiting high expression variability. However, relying solely on gene expression analysis is insufficient to fully understand these mechanisms. Therefore, in this study, we propose combining gene expression analysis, its co-expression network, and flux balance-based metabolic models to gain a deeper understanding of the disease’s complexity. This integration has revealed several metabolic subsystems associated with COPD and established a concordance between flux values and gene expression in COPD-related clusters. It demonstrates how effectively computational approaches can capture the molecular and metabolic alterations underlying COPD and align with experimental outcomes. The findings also offer biomarkers and pathways that can serve as a foundation for the development of COPD treatments. In summary, this study highlights the efficacy of an integrative computational approach that merges gene expression analysis, co-expression networks, and flux balance-based metabolic models. This methodology establishes connections between gene expression and metabolic flux while providing a valuable framework for future computational investigations and therapeutic strategies targeting COPD.

关键词： Analytical models Computational modeling Biological system modeling Biomarkers Chronic obstructive pulmonary disease Biochemistry Complexity theory Gene expression

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One- and two-sided prediction intervals for future Pareto record values with applications

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Journal of Statistical Theory and Applications 2025年 1-26页

作者： Schmiedt, Anja B. Empacher, Christina Kamps, Udo Department of Computer Science and Mathematics Regensburg University of Applied Sciences Regensburg 93053 Germany Institute of Statistics RWTH Aachen University Aachen 52056 Germany

Based on upper record values in a series of observations over time from a Pareto distribution, an exact and several approximate one-sided and two-sided prediction intervals for the next record value (or for another future record value) to appear are studied and compared by means of an extensive simulation study. The performances of the prediction intervals are evaluated and recommendations are proposed for what method should be used in a given situation with respect to the form of the prediction interval on the one hand and to the tail behaviour on the other. The proposed methods are applied to insurance, environmental and sports data, where the respectively fitted Pareto distributions show a different tail behaviour. As a result, it is seen that although the number of observed record values in the presented real data applications is rather small, as it is usually the case, the selected prediction intervals are of practical use. © The Author(s) 2025.

关键词： Interval prediction Pareto distribution Real data analyses Record values Simulation study

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Identifying Competence Gaps and Student Struggle by Monitoring Testing Performance 25

Identifying Competence Gaps and Student Struggle by Monitori...

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Proceedings of the 6th European Conference on Software Engineering Education

作者： Axel Böttcher Veronika Thurner Department of Computer Science and Mathematics HM Hochschule München University of Applied Sciences München Germany

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