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Students' Test Anxiety and Performance in Introductory programming: Do Exam and Assessment Modalities Play a Role?

COMPUTER APPLICATIONS IN ENGINEERING EDUCATION 2025年第3期33卷

作者： Tomic, Bojan Stojanovic, Tatjana Antovic, Ilija Milic, Milos Univ Belgrade Fac Org Sci Belgrade Serbia

Many students encounter programming for the first time during undergraduate studies. programming, being different from conventional elementary and middle school subjects, maybe a source of anxiety and frustration for these students. It is already known that heightened anxiety can negatively impact test performance, that females are more test anxious than males, and that many introductory computer science courses suffer from high dropout rates. This is the second part of our two-fold study, which aims to answer whether exam and assessment modalities influence students' test anxiety and the (possible) correlation between test anxiety and performance in an introductory programming course. The research questions and most important findings are as follows: (i) female versus male students' test anxiety regarding a computer-based automatically assessed exam (female students exhibited higher levels of test anxiety than male students), (ii) test anxiety regarding a paper-based manually assessed exam versus test anxiety regarding a computer-based automatically assessed exam (test anxiety during a paper-based manually assessed programming exam was lower than the latter), (iii) general test anxiety versus test anxiety regarding a computer-based automatically assessed exam (only female students were more test anxious on a computer-based automatically assessed exam than in general), (iv) test anxiety regarding a paper-based manually assessed exam and exam score (no correlation found), and (v) test anxiety regarding a computer-based automatically assessed exam and exam score (statistically significant, moderately negative correlation found only for female students). Our findings suggest that efforts should be made toward addressing female students' test anxiety in introductory programming as their well-being and exam performance are affected the most.

关键词： automated assessment evaluation and assessment higher education programming test anxiety

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A Linear programming Bound for Sum-Rank Metric Codes

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IEEE TRANSACTIONS ON INFORMATION THEORY 2025年第1期71卷 317-329页

作者： Abiad, Aida Gavrilyuk, Alexander L. Khramova, Antonina P. Ponomarenko, Ilia Eindhoven Univ Technol Dept Math & Comp Sci NL-5612 AZ Eindhoven Netherlands Vrije Univ Brussel Dept Math & Data Sci B-1050 Ixelles Belgium Shimane Univ Interdisciplinary Fac Sci & Engn Matsue 6908504 Japan Univ Memphis Dept Math Sci Memphis TN 38152 USA Eindhoven Univ Technol Dept Math & Comp Sci NL-5612 AZ Eindhoven Netherlands Hainan Univ Sch Math & Stat Haikou 570228 Peoples R China

We derive a linear programming bound on the maximum cardinality of error-correcting codes in the sum-rank metric. Based on computational experiments on relatively small instances, we observe that the obtained bounds o... 详细信息

关键词： Measurement Codes Linear programming Extraterrestrial measurements Eigenvalues and eigenfunctions Vectors Upper bound Symmetric matrices programming Polynomials Sum-rank metric code linear programming bound sum-rank metric graph association scheme eigenvalues

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An interval two-stage robust stochastic programming under a bi-level multi-objective framework toward river basin water resources allocation

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COMPUTERS & OPERATIONS RESEARCH 2025年 180卷

作者： Tu, Yan Lu, Yongzheng Lev, Benjamin Wuhan Univ Technol Sch Safety Sci & Emergency Management Wuhan 430070 Peoples R China Drexel Univ LeBow Coll Business Philadelphia PA 19104 USA

The uncertainty stemming from hydrological variables and socio-economic parameters poses new challenges to river basin water resources allocation (RBWRA). Given the pressing need for efficient, environmentally friendly, and equitable solutions in RBWRA, a bi-level multi-objective interval two-stage robust stochastic programming (BLMOITRSP) model is proposed. This model aims to achieve the optimal balance among efficiency, eco-friendliness, and equity, collectively called the "3E". A novel hierarchical mixed water allocation mechanism is introduced within this model. The basin authority pursues the "3E" objectives at the macro- control level through administrative water allocation. Conversely, sub-areas as followers prioritize economic interests, striving for economic benefit maximization through water market allocation. Furthermore, uncertain parameters (e.g., water demand) are treated as interval parameters, employing interval two-stage robust stochastic programming (ITRSP) to address uncertainty issues and control systemic risks in the model. To solve the BLMOITRSP model, we present a bi-level interactive global equilibrium optimization algorithm, fusing with the modified particle swarm optimization (PSO) algorithm. The bi-level algorithm provides solutions tailored to the preferences of different decision-makers. The proposed model and method are also applied to the Hanjiang River Basin in China to demonstrate its feasibility and effectiveness. The results indicate that the proposed model effectively ensures the "3E" balance. The introduction of the hierarchical mixed water allocation mechanism proves conducive to promoting water distribution and enhancing economic benefits. ITRSP effectively controls the systemic risks of the model's impact on the basin's total economic benefit. The economic performance of each sub-area varies in response to different decision preferences under RBWRA schemes. Finally, the conclusions and future research directions are provided.

关键词： Interval two-stage robust stochastic programming Bi-level multi-objective optimization Decision preference River basin water resources allocation Hierarchical mixed water allocation mechanism

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Analyzing the Benefits of Optical Topology programming for Mitigating Link-Flood DDoS Attacks

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IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 2025年第1期22卷 146-163页

作者： Nance-Hall, Matthew Liu, Zaoxing Sekar, Vyas Durairajan, Ramakrishnan Univ Oregon Eugene OR 97403 USA Univ Maryland Comp Sci College Pk MD 20742 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA

Link-flood attacks (LFAs) overwhelm bandwidth on links in a network using traffic from many sources, which is indistinguishable from benign traffic. Unfortunately, traditional DDoS defenses are incapable of stopping such attacks and recently proposed software-defined solutions are ineffective. In this work, we observe a new opportunity for mitigating LFAs using optical networking advances. In essence, we envision new capabilities for topology programming, to scale capacity on-demand to avoid congestion and add new links to the network to create new paths for traffic during LFA incidents. Realizing these benefits of optical topology programming raises unique challenges;the search space for candidate topology configurations is very large and joint optimization of topology and routing is NP-hard. We present ONSET-a framework that tackles these challenges to lay a practical foundation for topology programming-based defenses against LFAs. We show that ONSET complements existing programmable network defenses and amplifies their benefits. We perform a what-if style analysis of ONSET by simulating a wide-ranging set of attacks, including terabit-scale attacks against every single link, on five networks with two different routing capabilities and observe that ONSET provides the means to mitigate congestion loss in more than 90% of the hundreds of diverse attack scenarios considered.

关键词： Network topology Topology programming Optical fiber communication Routing Optical fiber networks Computer crime Computer simulation experiments DDoS defense optical networks wide area networks

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Exponential neutrosophic programming contrivance in multiple criteria for type-2 uncertain multi-objective transportation problem

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 143卷

作者： Kumar, Anesh Singh, Pitam Motilal Nehru Natl Inst Technol Allahabad Dept Math Prayagraj 211004 Uttar Pradesh India

Exploration of type-2 uncertainty in decision-making contexts is ongoing, and this article takes a pioneering approach by introducing the integration of type-2 normal uncertain parameters in the formulation of multi-objective transportation problem. The transformation of the type-2 normal uncertain model into deterministic one is achieved through methods based on expected value chance constraint programming. The problem is further investigated within a neutrosophic framework, where exponential membership functions with optimistic, pessimistic, and mixed variations are considered. The Pareto optimal solutions are obtained using truth, indeterminacy, and falsity membership functions in confluence with exponential neutrosophic programming techniques, including optimistic, pessimistic, and mixed senses. These techniques offer a flexible approach, empowering the decision-maker to select confidence levels according to their priorities. They can then analyze the Euclidean distance of the obtained optimal values of the objective functions, enhancing their understanding of the results. The benefits of employing our suggested strategies in a practical sense are clearly demonstrated via numerical experimentation, and finally, a comparison of the proposed approaches is also shown based on Euclidean distance. Furthermore, MATLAB software (version R2023b, 64-bit (maci64)) utilized to develop the code to solve the suggested issue.

关键词： Multi-fold uncertainty Multi-objective transportation problem Non-linear exponential neutrosophic programming Exponential truth Indeterminacy and falsity membership function etc

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Sustainable and reliable design of autonomous driving lanes: A chance-constrained extended goal programming approach

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2025年 195卷

作者： Chen, Yu Wang, Wei Hua, Xuedong Wang, David Z. W. Wang, Jian Southeast Univ Sch Transportat 02 Si Pai Lou Nanjing 211189 Peoples R China Nanyang Technol Univ Sch Civil & Environm Engn 50 Nanyang Ave Singapore 639798 Singapore

As autonomous vehicles (AVs) advance and gradually integrate into urban roadways, managing traffic in mixed environments, where human-driven vehicles (HVs) coexist with AVs, has become a critical challenge. Previous research has developed several optimal network design models for autonomous driving lanes (ADLs) to partially address this issue. However, these studies largely overlook uncertainties in AV market share or travel demand, which may lead to either excessive investment in underutilized infrastructure or traffic inefficiencies with insufficient capacity. To address these challenges, this study introduces a chance constrained programming (CCP) approach with sample approximation to effectively incorporate such uncertainties while accommodating varying risk preferences. To harmonize various sustainable development goals, such as network efficiency and social equity, CCP is further combined with extended goal programming, forming the proposed chance-constrained extended goal programming (CCEGP) model in this study for sustainable and reliable ADL design. Additionally, the routing behaviors of HV and AV users, each with differing levels of traffic awareness, are unified within a mixed crossnested logit-based stochastic user equilibrium model, thereby enhancing behavioral realism and model generalizability. The heuristic coati optimization algorithm is modified for solving, and case studies are conducted to validate its applicability.

关键词： Autonomous Vehicle Autonomous Driving Lane Uncertain AV Market Share Chance-Constrained programming Chance-Constrained Extended Goal programming Social Equity

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Supply chain viability by integrating R-imperatives, product development, and design decisions: A stochastic programming framework

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2025年 136卷

作者： Darmian, Sobhan Mostafayi Sgarbossa, Fabio Fattahi, Mohammad Morande, Juan Pablo NTNU Dept Mech & Ind Engn Trondheim Norway Northumbria Univ Newcastle Business Sch Newcastle Upon Tyne England Auburn Univ Dept Ind & Syst Engn Auburn AL USA

This study focuses on operationalizing R-imperatives by integrating product development and design decisions in supply chains (SC) facing disruptive events. This approach leads to viable SCs that are sustainable, resilient to disruptions, adaptable to ongoing changes, and enhance circularity. We use a stochastic programming approach to propose this new planning framework. To measure SC resilience, we consider the operational cost increase during the recovery period following a potential disruption. Additionally, flexibility in product configuration after disruptions is introduced as a new contingency strategy in this study. Motivated by two European companies involved in a circular manufacturing initiative in Norway, we define disruption scenarios and assess the effectiveness of our framework. Our results show that SCs can improve both economically and in terms of circularity when R-imperatives, product development, and design decisions are integrated. Furthermore, under disruption events, SC resilience improves on average by 4.28 % and 5.07 % in these two case studies. We also highlight that budget savings from this approach can range between 2 % to 10 % when a SC adapts to increased demand.

关键词： Viability Product development Supply chain design Circular economy Two-stage stochastic programming

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Development of a Photo-Curing 3D Printer for Fabrication of Small-Scale Soft Robots With programming Spatial Magnetization

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第3期10卷 2766-2773页

作者： Li, Shishi Meng, Xianghe Shen, Xingjian Wang, Jinrong Xie, Hui Harbin Inst Technol State Key Lab Robot & Syst Harbin 150080 Peoples R China

The magnetic soft robot has potential applications in biomimetic, soft interaction, and biomedical fields. However, its functionality depends on deformation patterns and locomotion modes, challenging the fabrication of complex structures with precise magnetization. Therefore, we developed a programming magnetization photo-curing 3D printer for fabrication of small-scale soft robots. The printer integrates a three-dimensional magnetic field generator (3D-MFG) and a digital light processing photo-curing system. The 3D-MFG generates a high-strength (up to 80 mT) magnetic field through Halbach arrays (x-y plane) and a solenoid (z-axis), generating arbitrary uniform magnetic field with low energy consumption. Adhesion between the printed structure and the substrate was analyzed, and A real-time force-based printing control method is presented for precise optimization of key parameters, including layer thickness, approaching force, and separation speed, enhancing overall print quality and reliability in stacking of complex three-dimensional structures. Finally, a crawling robot mimicking inchworm gait, a swimming robot with butterfly-inspired motion, and a capsule robot for targeted drug delivery were fabricated by the developed system. These experimental results validated the printer's capability to fabricate highly complex structures, advancing the practical application of small-scale soft robots in biomimetic and biomedical fields.

关键词： Three-dimensional printing Solenoids Robots Coils Magnetization Magnetic separation Soft robotics Fabrication programming Curing Soft robot materials and design additive manufacturing biologically-inspired robots

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Machine Learning-Enabled Evolutionary Two-Stage Stochastic programming

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年

作者： Pan, Jeng-Shyang Song, Pei-Cheng Chu, Shu-Chuan Snasel, Vaclav Watada, Junzo Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing Peoples R China Chaoyang Univ Technol Dept Informat Management Taichung Taiwan Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Peoples R China VSB Tech Univ Ostrava Dept Comp Sci Ostrava Czech Republic Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Japan

Two-stage Stochastic programming (2SP) is an effective framework for decision-making and modeling under uncertainty. Some 2SP problems are challenging due to their high dimensionality and nonlinearity. Machine learning can assist in solving 2SP problems by providing data-driven insights and approximations. Evolutionary algorithms are more general and effective methods for handling various 2SP problems by exploiting their structures and features. However, there is still a research gap in combining machine learning and evolutionary algorithms for solving 2SP problems. Therefore, this paper proposes for the first time a Machine Learning-enabled Evolutionary 2SP framework (MLE2SP), which uses machine learning to construct surrogate-assisted evolutionary optimization frameworks for 2SP. It constructs a novel multi-output 2SP surrogate model that considers scenarios and decision variables of both stages for the first time and proposes a data conversion method to handle the high-dimensional decision variables and scenarios. It also proposes a Machine Learning-enabled Differential Evolution Sampling (MLDES) method to update candidate solutions, which extracts knowledge from dominant candidate solutions to guide the evolutionary direction. Moreover, this work provides open sources of linear and nonlinear two-stage stochastic mixed-integer programming problem instances as benchmark test functions. The effectiveness and generality of the proposed algorithm and framework are verified by the test results on the benchmark test functions and a disaster relief logistics problem, which provide a new research direction for designing general and effective two-stage stochastic programming solving frameworks.

关键词： Stochastic processes programming Machine learning Evolutionary computation Machine learning algorithms Linear programming Random variables Metaheuristics Heuristic algorithms Decision making Stochastic programming machine learning evolutionary computation surrogate model uncertainty

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Exploring the Landscape of programming Language Identification With Machine Learning Approaches

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IEEE ACCESS 2025年 13卷 23556-23579页

作者： Verma, Amandeep Saha, Rahul Kumar, Gulshan Brighente, Alessandro Conti, Mauro Kim, Tai-Hoon Lovely Profess Univ Sch Comp Applicat Phagwara 144001 Punjab India Univ Padua Dept Math I-35131 Padua Italy Lovely Profess Univ Sch Comp Sci & Engn Phagwara 144001 Punjab India Chonnam Natl Univ Sch Elect & Comp Engn Gwangju 61186 South Korea

The increasing complexity of modern software development necessitates tools and methodologies for code analysis, maintenance, and migration in multi-language Integrated Development Environments (IDEs). The security needs of the software development process also recently led to the introduction of the Software Bill of Materials (SBOM), which vendors must include in their products. Automated tools become imperative to speed up software production and distribution. In the present code development landscape, automated programming Language Identification (PLI) helps to develop secure software development with its in-depth insights into programming consequences for code maintenance, legacy system management, code analysis, quality assurance, software modernization, migration, and code search. With the increasing success of Machine Learning (ML) models in detection methods, researchers use them for PLI showing superior performance of detection compared to the basic method of identification based on the source file extension. In this paper, we present the first survey for ML-based PLI methods providing insights into the domain's present status and guiding towards a futuristic tool development for PLI. We evaluate various ML techniques that recognize programming languages. These techniques include conventional, operation-based, and data source-based techniques. Our study examines the advantages and limitations of identifying the programming language in text, images, and videos. Our investigation also emphasizes the capabilities of the existing solutions that improve software development practices. Furthermore, our survey analyzes the strengths and limitations of existing code detection tools such as GitHub's Linguist, Pygments, ***, Ace, Google's Code Prettify, SourcererCC, Guesslang, and SonarLint. We also provide some open research problems for future researchers.

关键词： Codes Computer languages Software programming Feature extraction Syntactics Surveys Source coding Maintenance Machine learning programming language detection machine learning code software

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