In the challenging field of introductory programming, high enrolments and failure rates drive us to explore tools and systems to enhance student outcomes, especially automated tools that scale to large cohorts. This p...
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ISBN:
(纸本)9798400704239
In the challenging field of introductory programming, high enrolments and failure rates drive us to explore tools and systems to enhance student outcomes, especially automated tools that scale to large cohorts. This paper presents and evaluates the dcc --help tool, an integration of a Large Language Model (LLM) into the Debugging C Compiler (DCC) to generate unique, novice-focused explanations tailored to each error. dcc --help prompts an LLM with contextual information of compile- and run-time error occurrences, including the source code, error location and standard compiler error message. The LLM is instructed to generate novice-focused, actionable error explanations and guidance, designed to help students understand and resolve problems without providing solutions. dcc --help was deployed to our CS1 and CS2 courses, with 2,565 students using the tool over 64,000 times in ten weeks. We analysed a subset of these error/explanation pairs to evaluate their properties, including conceptual correctness, relevancy, and overall quality. We found that the LLM-generated explanations were conceptually accurate in 90% of compile-time and 75% of run-time cases, but often disregarded the instruction not to provide solutions in code. Our findings, observations and reflections following deployment indicate that dcc --help provides novel opportunities for scaffolding students' introduction to programming.
Graph Neural Networks (GNNs), which gained popularity recently, is facing the problem of reducing the cost of acquiring large datasets. Although a portion of the work combining GNN with active learning has been modera...
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This poster introduces ChartCode, a cloud-based tool designed for introductory programming courses that enables students to code by interacting with flowcharts. With a simple web interface, ChartCode allows users to c...
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Background Integrating programming in K-12 curriculum has become a global consensus. Teachers are central figures in programming instruction. But the majority of current research focuses on teachers' external teac...
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Background Integrating programming in K-12 curriculum has become a global consensus. Teachers are central figures in programming instruction. But the majority of current research focuses on teachers' external teaching behaviours and less on teachers' attitudes towards *** The purpose of this study is to validate the K-12 in-service Teacher programming Attitudes Scale (TPAS), to analyse teachers' programming attitudes and their differences in personal, teaching, and environmental factors, and further explore the predictive *** The sample is 888 K-12 teachers from China. First, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were adopted to validate the availability and reliability of TPAS;Second, independent sample t-test and one-way Analysis of Variance (ANOVA) were adopted to explain the differences. Finally, correlation analysis and linear regression analysis were adopted to verify the correlation and prediction *** and Conclusion TPAS possess good reliability and validity (chi(2)/df = 2.488, RMSEA = 0.055, CFI = 0.943, TLI = 0.935, and IFI = 0.943). Further analysis found that, first of all, male teachers possess more positive programming attitudes than female teachers;second, young teachers who are under 30 and the length of teaching under 5 years possess more positive programming attitudes;thirdly, in addition to Information and Communication Technology teachers, primary science teachers also possess high programming attitudes, followed by teachers of Humanities and Arts, reflecting the potential of teaching programming in integrated subjects and the humanities. Finally, primary school teachers possess the best programming attitudes. With the growth of grade, teachers' enthusiasm for programming has gradually decreased. Additionally, discipline, grade and gender factors were considered to predict K-12 teachers' programming *** The results of this study contribute to
Courses with programming assignments have long faced the issue of academic integrity violations (AIV) where cheating could harm the outcome of student learning. Checking code similarity in students' final submissi...
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In ITS, for students' success in carrying out their activities, it is essential to provide scaffolding, such as hints and feedback. Although using the ITS has increased student engagement and effort in the classro...
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ISBN:
(纸本)9783031328824;9783031328831
In ITS, for students' success in carrying out their activities, it is essential to provide scaffolding, such as hints and feedback. Although using the ITS has increased student engagement and effort in the classroom, some students have responded to the ITS's support facilities with an inappropriate behavior called gaming the system. Thus, in this article, we explore the phenomenon of gaming the system behavior, studying underlying factors related to automatically detecting when students game the system. We aim to develop a model for detecting gaming the system behavior in a computerprogramming student during problem-solving activities. Particularly, we have had special attention on testing the influence of variables such as (i) the student's level of belief and the system's level of belief in the difficulty level of the problem in detecting gaming the system;and (ii) partial submission of the problem associated with complete submission, in program form, in the detection of system manipulation. To conduct the detector's development, we developed an appropriate environment for data collection and preparation of these data to be used by supervised machine learning algorithms, allowing the detection of the behavior. The development of the detector involved training single and ensemble machine learning algorithms to classify the system's gaming behavior and obtain models with the best accuracy, including exploring, evaluating, and comparing different algorithms. The results show, considering the average of all algorithm results concerning all measures, the XGBoost ensemble classifier achieved the best performance.
Concurrent Constraint programming (CCP) originated in the late '80s with Vijay A. Saraswat's work. In the first '00s, a "soft" perspective of the constraint store based on a parametric algebraic ...
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ISBN:
(纸本)9783031737084;9783031737091
Concurrent Constraint programming (CCP) originated in the late '80s with Vijay A. Saraswat's work. In the first '00s, a "soft" perspective of the constraint store based on a parametric algebraic structure (a c-semiring) was proposed, namely soft CCP (SCCP). This paper enhances this SCCP language with local constraint spaces, where agents can see and interact with only a portion of the information stored. Thus, it is possible to represent areas where an agent can perform operations without affecting other local spaces. The resulting language is security-oriented as actions are checked against (e.g., read/write) rights, and it is quite rich because of nonmonotonic operations in the store (e.g., the removal of constraints is allowed), thus making the coordination of several agents more flexible and adaptive to personal and global goals.
The rapid growth of technology and computerscience, which has led to a surge in demand for skilled professionals in this field. The skill set required for computerscience jobs has evolved rapidly, creating challenge...
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The rapid growth of technology and computerscience, which has led to a surge in demand for skilled professionals in this field. The skill set required for computerscience jobs has evolved rapidly, creating challenges for those already in the workforce who need to adapt their skills quickly to meet industry demands. To stay ahead of the curve, it is essential to understand the hottest skills needed in the field. The article introduces a new method for analyzing job advertisements using social network analysis to identify the most critical skills required by employers in the *** this research, to form the communication network of skills, first 5763 skills were collected from the LinkedIn social network, then the relationship between skills was collected and searched in 7777 computerscience job advertisements, and finally, the balanced communication network of skills was formed. The study analyzes the formed communication network of skills in the computerscience job market and identifies four distinct communities of skills: Generalists, Infrastructure and Security, Software Development, and Embedded Systems. The findings reveal that employers value both hard and soft skills, such as programming languages and teamwork. Communication skills were found to be the most important skill in the labor market. Additionally, certain skills were highlighted based on their centrality indices, including communication, English, SQL, Git, and business skills, among others. The study provides valuable insights into the current state of the computerscience job market and can help guide individuals and organizations in making informed decisions about skills acquisition and hiring practices.
Most existing Finite Element Method and the Material Point Method (FEM-MPM) coupling is designed for explicit solvers. By contrast, implicit schemes offer the advantage of substantially larger time steps while maintai...
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Most existing Finite Element Method and the Material Point Method (FEM-MPM) coupling is designed for explicit solvers. By contrast, implicit schemes offer the advantage of substantially larger time steps while maintaining enhanced stability, particularly beneficial for tackling stiff nonlinear problems. Despite this, the development of implicit FEM-MPM coupling has not been extensively explored, leaving a notable gap in the context of contact and elastoplastic deformation challenges. Thus, this paper proposes a novel unified FEM-MPM coupling approach within implicit time integration under the framework of multivariable variational principle and convex cone programming, termed CP-FEMP. The CP-FEMP is the first successful attempt to impose the contact constraints via Lagrange multiplier and barrier method under convex cone programming, which can tackle not only the tie constraints but also the frictional contact between MPM and FEM domains with ensuring convergence and feasibility regardless of the time step size or the mesh resolutions. The contact locking issue in tie contact is circumvented using a well-defined interpolation space. The governing equations, associated frictional contact model, and associated elastoplastic constitutive law are formulated into a global convex optimisation problem, which is efficiently solved using primal-dual interior-point method. Through a succession of standard contact and elastoplastic benchmarks, the CP-FEMP demonstrates its proficiency in the precise transference of contact forces across MPM and FEM domains while showcasing commendable energy conservation attributes. Finally, the CP-FEMP is applied to a slope-retaining wall interaction problem. All results demonstrate CP-FEMP provides a comprehensive solution for FEM-MPM coupling, allowing for large incremental step under nonlinear contact , elastoplastic large deformation and guaranteeing strict, hard non -penetration conditions without convergence issues.
The growing importance of wearable technology in ice and snow sports highlights its role in injury prevention, where environmental hazards elevate injury risks. To address this, we propose a decision-making model usin...
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The growing importance of wearable technology in ice and snow sports highlights its role in injury prevention, where environmental hazards elevate injury risks. To address this, we propose a decision-making model using interval-valued bipolar fuzzy programming (IVBFP) for the optimal selection of wearable devices focused on athlete safety. The model employs multi-criteria decision-making (MCDM) methods to evaluate critical factors such as comfort, safety, durability, and real-time monitoring. Fuzzy logic enhances the precision and consistency of decision-making. The IVBFP model addresses vital challenges, including the diverse performance metrics of wearable devices and the uncertainty in expert evaluations. In comparison analyses, the model exhibited a 15% enhancement in judgment accuracy and a 12% decrease in uncertainty relative to conventional techniques. The results underscore the model's proficiency in correctly forecasting devices that mitigate injury risks, providing improved athlete protection. The approach effectively incorporates expert viewpoints and subjective evaluations, diminishing harm risk in simulated and actual datasets. This research is significant both theoretically and practically. It offers a comprehensive framework to guarantee athlete safety in extreme conditions, connecting scholars and practitioners.
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