programming is an important skill for different areas of knowledge. While in the past, programming skills were much more related to fields of computersciences and engineering, today, professionals from different area...
详细信息
programming is an important skill for different areas of knowledge. While in the past, programming skills were much more related to fields of computersciences and engineering, today, professionals from different areas benefit from the ability to write codes for different applications. Furthermore, programming stimulates logical thinking, which impacts other personal abilities. Health science students have limited exposure to programming during their studies. Aware of this and considering the prolonged time in social distancing in Brazil due to the SARS-COV2 pandemic in 2020, we organized an outreach course dedicated to teaching introductory concepts of programming for health science students. The activity was developed fully online using the Zoom web conference agent, lasting 12 wk (8 synchronous classes, 15 synchronous hours in total), and attended by 27 undergraduate and graduate students from two different universities. A collaborative problem-based learning and group-learning methodology were developed through asynchronous homework and mainly online synchronous activities. In this article, we describe our approach and provide some suggestions for replicating the course in other universities. We observed that the activities of the outreach course improved programming skills and confidence for most of the students. More importantly, it piqued their interest enough to motivate them to continue to practice writing and testing their programs. We concluded that an outreach course dedicated to programming promoted improvements in programming skills in health science students. Furthermore, the program was an opportunity to keep the students active in science while working from their homes during the pandemic.
From the Cradle of computer Graphics at the University of Utah to Pixar Animation Studios, my 50+ year professional career has been a parabola of many groundbreaking projects, as well as startup companies-some that ha...
详细信息
From the Cradle of computer Graphics at the University of Utah to Pixar Animation Studios, my 50+ year professional career has been a parabola of many groundbreaking projects, as well as startup companies-some that have become household names, including Br empty set derbund Software, Gracenote/SONY, and others. It has been an exciting and rewarding journey not only because of the bleeding edge technology and innovative products I worked on, but also due to the privilege of working with, managing, and leading people in these companies. I reflect on part of this journey in this article and my book "Managing The Unmanageable".
Mixed integer linear programming(MILP)is an NP-hard problem,which can be solved by the branch and bound algorithm by dividing the original problem into several subproblems and forming a search *** each subproblem,line...
详细信息
Mixed integer linear programming(MILP)is an NP-hard problem,which can be solved by the branch and bound algorithm by dividing the original problem into several subproblems and forming a search *** each subproblem,linear programming(LP)relaxation can be solved to find the bound for making the following ***,with the increasing dimension of MILPs in different applications,how to accelerate the solution process becomes a huge *** this survey,we summarize techniques and trends to speed up MILP solving from two ***,we present different approaches in simplex initialization,which can help to accelerate the solution of LP relaxation for each ***,we introduce the learning-based technologies in branch and bound algorithms to improve decision making in tree *** also propose several potential directions and extensions to further enhance the efficiency of solving different MILP problems.
Tangible programming languages (TPL) involve physical objects, often interlocking blocks, that represent computerprogramming elements. Users connect TPL blocks in logical chains to construct code that typically contr...
详细信息
ISBN:
(纸本)9798400704857
Tangible programming languages (TPL) involve physical objects, often interlocking blocks, that represent computerprogramming elements. Users connect TPL blocks in logical chains to construct code that typically controls the behavior of another device. Designed for young children, they offer a playful and embodied approach to computerscience education. While these systems can effectively teach basic programming concepts, TPL code lacks transferability and expressivity, which limits the types of problems that learners can engage with. Our work explores how designing for the multimodality of TPLs can support greater complexity in programming concepts, a smoother transition to advanced programming settings, and enhanced learner expression. We present touchBase, a TPL that leverages concepts in physical computing and principles of Gestalt psychology to support a culturally-sustaining approach to CS learning design.
Background and Context: A continued gender disparity has driven a need for effective interventions for recruiting girls to computerscience. Prior research has demonstrated that middle school girls hold beliefs and at...
详细信息
Background and Context: A continued gender disparity has driven a need for effective interventions for recruiting girls to computerscience. Prior research has demonstrated that middle school girls hold beliefs and attitudes that keep them from learning computerscience, which can be mitigated through classroom design. Objective: This study investigated whether programming environment design has a similar effect, to assess the potential utility of block-based programming (Scratch) for recruiting girls to computerscience compared to traditional text-based programming (Python). Method: One hundred and eighty-seven upper elementary and middle school students were surveyed to understand stereotype concern, sense of belonging, interest, and self-efficacy at baseline and after being shown each programming environment. Findings: Results indicated that stereotype concern was high for girls across all three conditions. Significantly more girls than boys showed interest in learning computerscience in Scratch compared to Python. Belonging, interest, and self-efficacy were inter-correlated for both genders.
We propose a quantum programming paradigm where all data are familiar classical data, and the only non-classical element is a random number generator that can return results with negative probability. Currently, the v...
详细信息
ISBN:
(纸本)9789819789429;9789819789436
We propose a quantum programming paradigm where all data are familiar classical data, and the only non-classical element is a random number generator that can return results with negative probability. Currently, the vast majority of quantum programming languages instead work with quantum data types made up of qubits. The description of their behavior relies on heavy linear algebra and many interdependent concepts and intuitions from quantum physics, which takes dedicated study to understand. We demonstrate that the proposed view of quantum programming explains its central concepts and constraints in more accessible, computationally relevant terms. This is achieved by systematically reducing everything to the existence of that negative-probability random generator, avoiding mention of advanced physics. This makes quantum programming more accessible to programmers without a deep background in physics or linear algebra. The bulk of this paper is written with such an audience in mind. As a working vehicle, we lay out a simple quantum programming language under this paradigm, showing that not only can it express all quantum algorithms, it also naturally captures the semantics of measurement without ever mentioning qubits or collapse.
To solve non-convex nonlinear programming problems, a double center swarm exploring varying parameter neurodynamic network (DCSE-VPNN) is proposed and analyzed. Firstly, a varying parameter neurodynamic network is pro...
详细信息
To solve non-convex nonlinear programming problems, a double center swarm exploring varying parameter neurodynamic network (DCSE-VPNN) is proposed and analyzed. Firstly, a varying parameter neurodynamic network is proposed as a solver for nonlinear programming to seek local optimal solutions. Secondly, a double center particle swarm optimization algorithm is exploited, wherein each neural network serves as a particle. Each particle independently explores a local optimal solution. Through information exchange among particles, the subsequent positions to be explored are updated. Asa result, DCSE-VPNN acquires the capability of global search. computer simulation experiments verify the efficacy of the proposed approach in solving non-convex nonlinear programming problems. In comparison with two existing methods, the results show that the proposed DCSE-VPNN approach has fewer iterations and higher search accuracy.
Early intervention is critical in increasing student success in computerscience (CS) courses, which have attracted a diverse student population. In-class exercises, which are often low-stake and quick assignments, ar...
详细信息
ISBN:
(纸本)9798400705328
Early intervention is critical in increasing student success in computerscience (CS) courses, which have attracted a diverse student population. In-class exercises, which are often low-stake and quick assignments, are a popular method for active learning and formative assessment. This study explores the potential of using in-class coding exercises for early intervention in programming courses, particularly before midterm exams. We analyzed historical data from a CS1 course to evaluate whether in-class coding exercises can predict midterm exam performance. Our findings reveal that in-class coding exercises are effective predictors of midterm performance and can serve as valuable tools for early intervention. Specifically, exercise scores and time on task are sufficient indicators of student performance. Although in-class exercises are less powerful predictors than traditional metrics, they offer quicker actionable insights. Additionally, predicting students who are struggling is more feasible than forecasting those who will fail or achieve specific letter grades. This research underscores the potential for designing targeted intervention schemes to support students in CS1 and other programming courses, highlighting the importance of timely and data-driven support mechanisms.
The rapid advancement of Large Language Models (LLMs) like ChatGPT has raised concerns among computerscience educators about how programming assignments should be adapted. This paper explores the capabilities of LLMs...
详细信息
ISBN:
(纸本)9798400705311
The rapid advancement of Large Language Models (LLMs) like ChatGPT has raised concerns among computerscience educators about how programming assignments should be adapted. This paper explores the capabilities of LLMs (GPT-3.5, GPT-4, and Claude Sonnet) in solving complete, multi-part CS homework assignments from the SIGCSE Nifty Assignments list. Through qualitative and quantitative analysis, we found that LLM performance varied significantly across different assignments and models, with Claude Sonnet consistently outperforming the others. The presence of starter code and test cases improved performance for advanced LLMs, while certain assignments, particularly those involving visual elements, proved challenging for all models. LLMs often disregarded assignment requirements, produced subtly incorrect code, and struggled with context-specific tasks. Based on these findings, we propose strategies for designing LLM-resistant assignments. Our work provides insights for instructors to evaluate and adapt their assignments in the age of AI, balancing the potential benefits of LLMs as learning tools with the need to ensure genuine student engagement and learning.
In this poster we propose an approach to designing auto-graded programming course projects that are modular and easily manageable by an instructor. Based on our experiences with the Sail() platform which supports auto...
详细信息
ISBN:
(纸本)9798400706035
In this poster we propose an approach to designing auto-graded programming course projects that are modular and easily manageable by an instructor. Based on our experiences with the Sail() platform which supports auto-grading and feedback generation in multiple contexts, we design the approach to overcome the challenges we observed. The approach is especially focused on designing projects that can be utilized by multiple instructors who may have various scopes or students with varying backgrounds. The approach enables differentiated learning-thereby improving learning experiences and outcomes. We also discuss challenges of using such a modular approach to auto-graded projects.
暂无评论