The recent, widespread availability of Large Language Models (LLMs) like ChatGPT and GitHub Copilot may impact introductory programming courses (CS1) both in terms of what should be taught and how to teach it. Indeed,...
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ISBN:
(纸本)9798400706004
The recent, widespread availability of Large Language Models (LLMs) like ChatGPT and GitHub Copilot may impact introductory programming courses (CS1) both in terms of what should be taught and how to teach it. Indeed, recent research has shown that LLMs are capable of solving the majority of the assignments and exams we previously used in CS1. In addition, professional software engineers are often using these tools, raising the question of whether we should be training our students in their use as well. This experience report describes a CS1 course at a large research-intensive university that fully embraces the use of LLMs from the beginning of the course. To incorporate the LLMs, the course was intentionally altered to reduce emphasis on syntax and writing code from scratch. Instead, the course now emphasizes skills needed to successfully produce software with an LLM. This includes explaining code, testing code, and decomposing large problems into small functions that are solvable by an LLM. In addition to frequent, formative assessments of these skills, students were given three large, open-ended projects in three separate domains (data science, image processing, and game design) that allowed them to showcase their creativity in topics of their choosing. In an end-of-term survey, students reported that they appreciated learning with the assistance of the LLM and that they interacted with the LLM in a variety of ways when writing code. We provide lessons learned for instructors who may wish to incorporate LLMs into their course.
Contribution: Understanding pupils' conceptualization of robots and programming can help teachers to avoid a disconfirmation experience by selecting more appropriate educational tools, robot designers in improving...
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Contribution: Understanding pupils' conceptualization of robots and programming can help teachers to avoid a disconfirmation experience by selecting more appropriate educational tools, robot designers in improving the robot design, and researchers in further improvement of the field. Background: Human-robot interaction (HRI) is affected by the actual but also expected robot's appearance and capabilities. Multiple factors, such as age, gender, media exposure, ICT exposure, or culture, influence mental models regarding robots;therefore, it is important to investigate those for a specific cohort in the designated geographical area. Research Questions: The mental models regarding robots and programming, and the way they are possibly biased by popular culture, exposure to ICT or parental influence were studied. Research questions concerned cognitive elements of mental models, namely, definitions and knowledge of robots, and programming and how those progress in time. Also, the research studied a figurative aspect of mental models regarding robots, with a focus on anthropomorphic features. Methodology: To research the influence of the short-term HRI, four classes of eight to nine years old elementary school pupils were included in a workshop where pre and postquestionnaires were used as research instruments. Besides pupils, later in this two-phase longitudinal research, after a year of formal education in Informatics, research instruments also included teachers and parents, to investigate their influence on children's mental models. Findings: The change of mental models under the influence of the one-time workshop was not permanent. However, a combination of maturation with informal and formal intervention supports the conceptualization of programming.
Given a connected undirected graph G = (V, E), let G[S] be the subgraph of G induced by the set of vertices S subset of V. The Chordless Cycle Problem (CCP) consists in finding a subset S subset of V of maximum cardin...
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ISBN:
(纸本)9783031609237;9783031609244
Given a connected undirected graph G = (V, E), let G[S] be the subgraph of G induced by the set of vertices S subset of V. The Chordless Cycle Problem (CCP) consists in finding a subset S subset of V of maximum cardinality such that G[S] is a chordless cycle. We present a Quadratically Constrained reformulation for the CCP, derive a Semidefinite programming (SDP) relaxation for it and solve that relaxation by Lagrangian Relaxation (LR). Compared to previously available dual bounds, our SDP bounds resulted to be quite strong. We then introduce a hybrid algorithm involving two combined phases: the LR scheme, which acts as a warm starter for a Branch-and-cut (BC) algorithm that follows it. In short, the LR algorithm allows us to formulate a finite set of SDP cuts that can be used to retrieve the SDP bounds in a Linear programming relaxation for the CCP. Such cuts are not ready to be used by the BC as they are formulated in an extended variable space. Thus, the BC projects them back onto the original space of variables and separates them by solving a Linear Program. On dense input graphs, our proposed BC algorithm, in its current preliminary state of development, already outperforms its competitors in the literature.
This paper addresses an integrated rack assignment and robot routing problem arising in robotic movable fulfillment systems (RMFS). This NP-hard planning task goes beyond current literature by simultaneously optimizin...
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This paper addresses an integrated rack assignment and robot routing problem arising in robotic movable fulfillment systems (RMFS). This NP-hard planning task goes beyond current literature by simultaneously optimizing movable rack selection and multi-agent collision-free path finding, rather than decomposing them. A mixed integer programming (MIP) model with a new level-space-time network representation is proposed, jointly considering reusable racks, robot-rack pairings, storage repositioning, and collision avoidance. To improve computational efficiency, a fast rolling horizon heuristic and greedy algorithm are developed. Extensive experiments demonstrate that the integrated method's solutions can improve by 30 % upon conventional decomposed approaches. Intriguing test cases reveal the model, suggesting non-intuitive robot carryover policies that are unfound by separate selection and routing methods. This indicates potential optimization benefits from explicitly coordinating task assignment, scheduling, and routing decisions in complex automated warehousing systems. The rolling horizon heuristic solutions approach optimality with much greater efficiency than directly solving one large MIP, validating its practical value. This research provides useful integrated modeling insights, efficient solution algorithms, and decision support for efficiently controlling next-generation robotic movable fulfillment systems.
This Innovative Practice Full Paper presents our design of teaching computerprogramming for middle and high school students during an one-week Summer camp for the past five years, with an interruption in the Summer o...
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ISBN:
(纸本)9798350336429
This Innovative Practice Full Paper presents our design of teaching computerprogramming for middle and high school students during an one-week Summer camp for the past five years, with an interruption in the Summer of 2020 due to the Covid-19 pandemic. Many researchers believe that good Summer camps can improve many students' educational and career development outcomes. Teachers and administrators are increasingly promoting Summer camp opportunities for introducing programming skills to middle and high school students. The motivation of our program is to offer hands-on projects for middle and high school students to increase their interests and knowledge in computing to meet the growing demand. Like some Summer coding camps, we picked Scratch as the programming language (designed and offered for free by the Massachusetts Institute of Technology) for the students to learn important mathematical and computerprogramming concepts. In addition, the students learn how to think and reason creatively, reason systematically, and work collaboratively, while also having fun during the one-week Summer camp. For the students already familiar with Scratch, the instructor exposed students to basic concepts of Java programming language, such as Java virtual machine, computer memory, data representation, primitive data types, casting, arithmetic, and relational operators, as well as the assignment, selection and printing statements. This article presents our findings from Summer camps organized in 2018 and 2022. Our conclusion is that all students showed a better understanding of programming concepts and confidence in computing. In the upcoming paper sections, we will describe details about how we made one-week camps to be unique compared to other similar camps. One element of our own approach is to teach in an effective and innovative using an interactive teaching approach. For example, when we designed the lecture notes, we imagine that we are students taking for the first time
In this paper, we investigate the influence of various factors, such as programming language, testing environment, and input data, on the accuracy of algorithm execution measurements. To conduct this study, we used th...
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programming for today's quantum computers is making significant strides toward modern workflows compatible with high performance computing (HPC), but fundamental challenges still remain in the integration of these...
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ISBN:
(数字)9783031617638
ISBN:
(纸本)9783031617621;9783031617638
programming for today's quantum computers is making significant strides toward modern workflows compatible with high performance computing (HPC), but fundamental challenges still remain in the integration of these vastly different technologies. Quantum computing (QC) programming languages share some common ground, as well as their emerging runtimes and algorithmic modalities. In this short paper, we explore avenues of refinement for the quantum processing unit (QPU) in the context of many-tasks management, asynchronous or otherwise, in order to understand the value it can play in linking QC with HPC. Through examples, we illustrate how its potential for scientific discovery might be realized.
One of the important roles of educators and academicians is to accurately assess students, and provide timely feedback to ensure that the learning outcomes of the academic program are achieved. Student performance pre...
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ISBN:
(数字)9798350367355
ISBN:
(纸本)9798350367355
One of the important roles of educators and academicians is to accurately assess students, and provide timely feedback to ensure that the learning outcomes of the academic program are achieved. Student performance prediction can help in this regard by predicting the future performance of the student based on the student's past performance benefitting from the data availability in the learning management system of the academic institution. This work aims to implement a machine learning model to predict the performance of students;particularly, students in the computerscience and Engineering majors, based on their past performance in general first-year courses, namely, English, Math, and programming. These particular courses were selected for the prediction purpose because they are essential in the academic progress of students in the computerscience and Engineering majors. Specifically, English is the study language, yet it is considered a second language for many students, which can pose challenges in perceiving the core technical courses. Besides, Math and programming courses are essential to understand the technical aspects of most computerscience and Engineering courses. Therefore, knowing how the student is performing in these first-year courses will give important insights on how the student will perform later in the study program. In this work, the student performance is indicated by the Cumulative Grade Point Average (CGPA) which is predicted using polynomial regression based on a real dataset. The utilized dataset includes information about the CGPA and the grades of English, Math, and programming courses from different levels (i.e., fresh and senior students), and extracted from the learning and management system of an academic institution. It is worth to mention that different degrees of the polynomial regression were investigated to observe how well the curve fits the data. To evaluate the prediction performance, the R-squared value is used. Results sh
A Low-rank Spectral Optimization Problem (LSOP) minimizes a linear objective function subject to multiple two-sided linear inequalities intersected with a low-rank and spectral constrained domain. Although solving LSO...
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ISBN:
(纸本)9783031598340;9783031598357
A Low-rank Spectral Optimization Problem (LSOP) minimizes a linear objective function subject to multiple two-sided linear inequalities intersected with a low-rank and spectral constrained domain. Although solving LSOP is, in general, NP-hard, its partial convexification (i.e., replacing the domain by its convex hull) termed "LSOP-R", is often tractable and yields a high-quality solution. This motivates us to study the strength of LSOP-R. Specifically, we derive rank bounds for any extreme point of the feasible set of LSOP-R with different matrix spaces and prove their tightness. The proposed rank bounds recover two well-known results in the literature from a fresh angle and allow us to derive sufficient conditions under which the relaxation LSOP-R is equivalent to LSOP. To effectively solve LSOP-R, we develop a column generation algorithm with a vector-based convex pricing oracle, coupled with a rank-reduction algorithm, which ensures that the output solution always satisfies the theoretical rank bound. Finally, we numerically verify the strength of LSOP-R and the efficacy of the proposed algorithms.
A trained regression model can be used to create new synthetic training data by drawing from a distribution over independent variables and calling the model to produce a prediction for the dependent variable. We inves...
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ISBN:
(数字)9783031539695
ISBN:
(纸本)9783031539688;9783031539695
A trained regression model can be used to create new synthetic training data by drawing from a distribution over independent variables and calling the model to produce a prediction for the dependent variable. We investigate how this idea can be used together with genetic programming (GP) to address two important issues in regression modelling, interpretability and limited data. In particular, we have two hypotheses. (1) Given a trained and non-interpretable regression model (e.g., a neural network (NN) or random forest (RF)), GP can be used to create an interpretable model while maintaining accuracy by training on synthetic data formed from the existing model's predictions. (2) In the context of limited data, an initial regression model (e.g., NN, RF, or GP) can be trained and then used to create abundant synthetic data for training a second regression model (again, NN, RF, or GP), and this second model can perform better than it would if trained on the original data alone. We carry out experiments on four well-known regression datasets comparing results between an initial model and a model trained on the initial model's outputs;we find some results which are positive for each hypothesis and some which are negative. We also investigate the effect of the limited data size on the final results.
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