Group or team projects are an essential component of the software engineering curriculum. Earlier studies have explored how prior programming experience influences students' team project performance and overall cl...
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ISBN:
(纸本)9798400704239
Group or team projects are an essential component of the software engineering curriculum. Earlier studies have explored how prior programming experience influences students' team project performance and overall class performance in software engineering. However, few studies address the impact of prior programming experience on students' contributions to team projects. Previous work has varied in its definitions of prior programming experience or skill, leading to inconsistent findings. In this study, we collected pre-class GitHub contribution metrics from 237 students (forming 79 teams of three) across two academic years to measure their prior programming experience and skills. We also mined students' project repositories' git logs to collect individual student contributions. A central question revolved around whether students with more substantial prior programming experience were indeed more active contributors to their project teams. Interestingly, our data indicated a positive correlation between prior programming experience and contributions to team projects. We further delved into team dynamics. Specifically, we questioned if teams made up of members with comparable skill levels exhibited a more even distribution of contributions. Contrary to expectations, our findings revealed no association between these two variables. Moreover, we investigated the team configurations that might encourage the rise of "free riders"-students who contributed only minimally. This paper seeks to augment the body of research on computing education and assist educators in understanding how prior programming experience impacts students' contributions in team projects.
One of the most time consuming, yet important tasks in academia is assessing and correcting student assignments. When it comes to programming, this task becomes even more complicated, as the evaluation involves compil...
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ISBN:
(纸本)9783031488023;9783031488030
One of the most time consuming, yet important tasks in academia is assessing and correcting student assignments. When it comes to programming, this task becomes even more complicated, as the evaluation involves compilation and execution correctness. In this demo, Diorthotis, a parallel, language-agnostic batch evaluator is presented which has the capacity to exploit the multiple cores available in modern processors so as to quickly compile, execute and evaluate assignments for hundreds of students only in a few seconds.
Robotics education has received widespread attention in K-12 education. Studies have pointed out that in robotics courses, learners face challenges in learning abstract content, such as constructing a robot with a goo...
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With the rising popularity of Object-Oriented programming (OOP) in both research and industry, it is important that computerscience students be educated in the fundamentals of OOP and what it can be used for. However...
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ISBN:
(纸本)9798350300543
With the rising popularity of Object-Oriented programming (OOP) in both research and industry, it is important that computerscience students be educated in the fundamentals of OOP and what it can be used for. However, OOP can be difficult for students to learn because of the complex interactions between objects and code. We believe that implementing a web-based programming tutorial system alongside traditional instruction may help students to better understand the fundamental principles of OOP and avoid common programming misconceptions.
programming literacy is crucial for current and future generations of young learners, irrespective of their career paths. programming education is thus essential, making teaching methods and tools to be tailored to th...
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ISBN:
(纸本)9798400712166
programming literacy is crucial for current and future generations of young learners, irrespective of their career paths. programming education is thus essential, making teaching methods and tools to be tailored to the target audience. In this context, contemporary visual programming environments, particularly block-based programming, have become instrumental in introducing programming concepts to young learners. Educational theories such as Constructionism advocate an approach centered on the learner to deepen and motivate learning. In computerscience, these theories can be applied by providing hands-on experiences that connect computerscience to real-life situations through the manipulation or construction of physical and tangible computational devices. This study explores the impact of creating a smart object for a smart home using block-based programming on young learners' attitudes and perceptions toward programming and their programming skills acquisition. An introductory programming workshop involved 28 8(th) grade students from a secondary school constructing and programming a smart-lighting object in a smart home setting. Performance, attitude, and perception trajectories were assessed through repeated questionnaires. Our results indicate that constructing and programming a real-life smart object enhances learners' confidence and programming skills. This paper contributes to programming education literature by demonstrating the potential of block-based programming, specifically in the context of state-of-the-art smart technologies, to foster programming skills and develop positive attitudes and perceptions among learners.
A Refute problem provides students with (1) a programming task and (2) a buggy solution (typically, a function) for that task. A student answers a Refute problem by specifying an input on which the function does not r...
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ISBN:
(纸本)9783031843907;9783031843914
A Refute problem provides students with (1) a programming task and (2) a buggy solution (typically, a function) for that task. A student answers a Refute problem by specifying an input on which the function does not return the expected result (as per the task). This paper makes three contributions. First, by identifying inconsistencies in scores to Refute problems produced manually by computerscience faculty, we demonstrate that it is challenging to manually score such problems. Second, we identify a key category of near-correct responses whose importance seems to be under-appreciated by faculty in our study. Together, these findings indicate a need for rubrics that can support automated grading and are easy to explain. As a third contribution, we propose RECE (rhyming with peace), a family of simple rubrics for autograding Refute problems. RECE rubrics reward near-correct responses from the above category, but they otherwise align with the scores suggested by faculty. We believe that these contributions will aid faculty in introducing Refute problems as assessment items in programming courses.
Last-mile delivery problems where trucks and drones collaborate to deliver goods to final customers are considered. We focus on settings where a fleet with several homogeneous trucks work in parallel to collaborative ...
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ISBN:
(纸本)9783031629112;9783031629129
Last-mile delivery problems where trucks and drones collaborate to deliver goods to final customers are considered. We focus on settings where a fleet with several homogeneous trucks work in parallel to collaborative drones, able to combine with each other to optimize speed and power consumption for deliveries. A heuristic for the min-max vehicle routing problem is coupled with constraint programming models, leading to an effective method able to provide several state-of-the-art solutions for the instances commonly adopted in the literature.
computerscience pedagogy, especially in the higher education and vocational training context, has long-favored the hands-on practice provided by programming tasks due to the belief that this leads to better performan...
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computerscience pedagogy, especially in the higher education and vocational training context, has long-favored the hands-on practice provided by programming tasks due to the belief that this leads to better performance on hands-on tasks at work. This assumption, however, has not been experimentally tested against other modes of engagement such as worked example-based reflection. While theory suggests that example-based reflection could be better for conceptual learning, the concern is that the lack of practice will leave students unable to implement the learned concepts in practice, thus leaving them unprepared for work. In this article, therefore, we experimentally contrast programming practice with example-based reflection to observe their differential impact on conceptual learning and performance on a hands-on task in the context of a collaborative programming project. The industry paradigm of Mob programming, adapted for use in an online and instructional context, is used to structure the collaboration. Keeping with the prevailing view held in pedagogy, we hypothesize that example-based reflection will lead to better conceptual learning but will be detrimental to hands-on task performance. Results support that reflection leads to conceptual learning. Additionally, however, reflection does not pose an impediment to hands-on task performance. We discuss possible explanations for this effect, thus providing an improved understanding of prior theory in this new computerscience education context. We also discuss implications for the pedagogy of software engineering education, in light of this new evidence, that impacts student learning as well as work performance in the future.
The history of computing usually focuses on achievements in Western universities and research centers and is mostly about what happened in the United States and Great Britain. However, in Eastern Europe, particularly ...
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The history of computing usually focuses on achievements in Western universities and research centers and is mostly about what happened in the United States and Great Britain. However, in Eastern Europe, particularly in war-torn Poland, where there was very little state funding, many highly original hardware and software projects were initiated. The small number of publications available to us, especially those in English, led to the belief that technological progress was the result of research carried out in Western countries alone. This article aims to fill this knowledge gap by focusing on the numerous research projects initiated in Polish universities and computer industries that unfortunately turned into dead ends as the result of socialist policies. These are references that cannot be ignored, not only for a historical reconstruction of the evolution of technology but also with regard to the social effects recorded in Poland immediately after the Second World War. The communist ideology, which pursued gender equality policies after the end of the war, encouraged women to pursue education, enabling the many female students enrolled in mathematics degree courses to specialize in "Maszyny Matematyczne" (mathematical machines) and become, like men, experts in computerprogramming and design. As well as highlighting the role that Poland played in the nascent "computerscience" and providing detailed information on what women contributed, this article will explain why the success of the Polish computer industry was limited due to the nonexistent coordination between the communist states (Comecon).
作者：
Kim, Jason Z.Bassett, Dani S.University of Pennsylvania
Department of Bioengineering Philadelphia USA (GRID:grid.25879.31) (ISNI:0000 0004 1936 8972) Cornell University
Department of Physics Ithaca USA (GRID:grid.5386.8) (ISNI:***) University of Pennsylvania
Departments of Bioengineering Physics & Astronomy Electrical & Systems Engineering Neurology and Psychiatry Philadelphia USA (GRID:grid.25879.31) (ISNI:0000 0004 1936 8972) Santa Fe Institute
Santa Fe USA (GRID:grid.209665.e) (ISNI:0000 0001 1941 1940)
From logical reasoning to mental simulation, biological and artificial neural systems possess an incredible capacity for computation. Such neural computers offer a fundamentally novel computing paradigm by representin...
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From logical reasoning to mental simulation, biological and artificial neural systems possess an incredible capacity for computation. Such neural computers offer a fundamentally novel computing paradigm by representing data continuously and processing information in a natively parallel and distributed manner. To harness this computation, prior work has developed extensive training techniques to understand existing neural networks. However, the lack of a concrete and low-level machine code for neural networks precludes us from taking full advantage of a neural computing framework. Here we provide such a machine code along with a programming framework by using a recurrent neural network—a reservoir computer—to decompile, code and compile analogue computations. By decompiling the reservoir’s internal representation and dynamics into an analytic basis of its inputs, we define a low-level neural machine code that we use to program the reservoir to solve complex equations and store chaotic dynamical systems as random-access memory. We further provide a fully distributed neural implementation of software virtualization and logical circuits, and even program a playable game of pong inside of a reservoir computer. Importantly, all of these functions are programmed without requiring any example data or sampling of state space. Finally, we demonstrate that we can accurately decompile the analytic, internal representations of a full-rank reservoir computer that has been conventionally trained using data. Taken together, we define an implementation of neural computation that can both decompile computations from existing neural connectivity and compile distributed programs as new *** neural networks are flexible architectures that can perform a variety of complex, time-dependent computations. Kim and Bassett introduce an alternative, ‘programming’-like computational framework to determine the appropriate network parameters for a specific task without the need fo
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