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Proceedings of the 16th Annual Conference on Information Technology Education最新文献

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Rebirth of IT Degree Program to Create Your Future Again IT学位课程的重生,再次创造你的未来
Pub Date : 2015-09-29 DOI: 10.1145/2808006.2808015
Sam Chung, T. Imboden, Andrey Soares, Nancy L. Martin, Belle Woodward
Sam Chung Info. Systems Technologies School of IS & Applied Technologies College of Applied Sciences & Arts Southern Illinois University Carbondale, IL samchung@siu.edu Tom Imboden Info. Systems Technologies School of IS & Applied Technologies College of Applied Sciences & Arts Southern Illinois University Carbondale, IL timboden@siu.edu Andrey Soares Info. Systems Technologies School of IS & Applied Technologies College of Applied Sciences & Arts Southern Illinois University Carbondale, IL asoares@siu.edu
山姆·钟信息。系统技术IS与应用技术学院应用科学与艺术学院南伊利诺伊大学卡本代尔,伊利诺伊州samchung@siu.edu Tom Imboden信息。系统技术IS与应用技术学院应用科学与艺术学院南伊利诺伊大学卡本代尔,IL timboden@siu.edu安德烈·苏亚雷斯信息。系统技术IS与应用技术学院应用科学与艺术学院南伊利诺伊大学卡本代尔,伊利诺伊州asoares@siu.edu
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引用次数: 0
Programming in Scratch and Mathematics: Augmenting Your Geometry Curriculum, Today! 编程在Scratch和数学:增加你的几何课程,今天!
Pub Date : 2015-09-29 DOI: 10.1145/2808006.2809636
Klaus-Tycho Förster
Algorithms and programming are and will be essential cultural techniques used in creating the future, yet most students barely encounter them in their Mathematics curriculum. Using geometry, we studied how to integrate programming and algorithms in the current curriculum in grades 6 and 7.
算法和编程是创造未来必不可少的文化技术,但大多数学生在数学课程中很少遇到它们。在六年级和七年级的课程中,我们运用几何的方法,研究了如何将编程和算法结合起来。
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引用次数: 5
Work In Progress: Machine Learning In Robotics 正在进行的工作:机器人中的机器学习
Pub Date : 2015-09-29 DOI: 10.1145/2808006.2808044
Greg Yera, Alexander Strong, Hannah Leleux, T. Holcombe, Colin Smith, Timothy Gonzales
This work investigates combining reinforcement learning and radial basis function neural network (RBFNN) learning to improve the performance of a robot that learns to perform its task. A simple task is given, and the simulator uses reinforcement learning to generate a q-learning table, which is then used to generate a neural network. Analysis is then done to determine if the combination improves the robots performance of its task
这项工作研究了将强化学习和径向基函数神经网络(RBFNN)学习相结合,以提高机器人学习执行任务的性能。给出一个简单的任务,模拟器使用强化学习生成一个q-学习表,然后使用该表生成一个神经网络。然后进行分析,以确定该组合是否提高了机器人的任务性能
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引用次数: 1
M-CAFE 1.0: Motivating and Prioritizing Ongoing Student Feedback During MOOCs and Large on-Campus Courses using Collaborative Filtering M-CAFE 1.0:在mooc和大型校园课程中使用协同过滤来激励和优先考虑正在进行的学生反馈
Pub Date : 2015-09-29 DOI: 10.1145/2808006.2808020
Mo Zhou, A. Cliff, S. Krishnan, Brandie Nonnecke, Camille Crittenden, Kanji Uchino, Ken Goldberg
During MOOCs and large on-campus courses with limited face-to-face interaction between students and instructors, assessing and improving teaching effectiveness is challenging. In a 2014 study on course-monitoring methods for MOOCs [30], qualitative (textual) input was found to be the most useful. Two challenges in collecting such input for ongoing course evaluation are insuring student confidentiality and developing a platform that incentivizes and manages input from many students. To collect and manage ongoing ("just-in-time") student feedback while maintaining student confidentiality, we designed the MOOC Collaborative Assessment and Feedback Engine (M-CAFE 1.0). This mobile-friendly platform encourages students to check in weekly to numerically assess their own performance, provide textual ideas about how the course might be improved, and rate ideas suggested by other students. For instructors, M-CAFE 1.0 displays ongoing trends and highlights potentially valuable ideas based on collaborative filtering. We describe case studies with two EdX MOOCs and one on-campus undergraduate course. This report summarizes data and system performance on over 500 textual ideas with over 8000 ratings. Details at http://m-cafe.org.
在mooc和学生与教师面对面交流有限的大型校内课程中,评估和提高教学效果是一项挑战。2014年一项关于mooc课程监控方法的研究发现,定性(文本)输入是最有用的。为正在进行的课程评估收集这些意见面临两个挑战,一是确保学生的机密性,二是开发一个激励和管理许多学生意见的平台。为了收集和管理正在进行的(“及时”)学生反馈,同时保持学生的机密性,我们设计了MOOC协作评估和反馈引擎(M-CAFE 1.0)。这个适合移动设备的平台鼓励学生每周签到,对自己的表现进行数字评估,提供关于课程如何改进的文本意见,并对其他学生提出的意见进行评分。对于教师来说,M-CAFE 1.0显示了正在进行的趋势,并突出了基于协同过滤的潜在有价值的想法。我们描述了两个EdX mooc课程和一个校园本科课程的案例研究。该报告总结了500多个文本思想的数据和系统性能,具有8000多个评级。详情请访问http://m-cafe.org。
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引用次数: 5
Serious Toys: Introducing Sensors and Sensor Networks in Pre-collegiate Classrooms 严肃玩具:在大学预科教室中引入传感器和传感器网络
Pub Date : 2015-09-29 DOI: 10.1145/2808006.2808037
Yvon Feaster, Jiannan Zhai, J. Hallstrom
Sensors have become pervasive throughout our daily activities. Virtually all appliances use one or more sensors. Networks of sensors are used to support home and medical monitoring, wildlife behavioral monitoring, military applications, and environmental management. Observing the value of sensors in computing, we developed the third in a series of serious toys for use in pre-collegiate classrooms. The "toy" is an embedded hardware device designed to enhance a lecture titled "Learning About Sensors and Sensor Networks." In this paper, we describe the curriculum module and its use in five classes from a local middle school, and four groups from a summer outreach program. We conclude with a summary of evaluation results that show the program produced positive results.
传感器在我们的日常生活中无处不在。几乎所有的电器都使用一个或多个传感器。传感器网络用于支持家庭和医疗监测、野生动物行为监测、军事应用和环境管理。观察到传感器在计算中的价值,我们开发了一系列严肃玩具中的第三个,供大学预科教室使用。这个“玩具”是一个嵌入式硬件设备,旨在加强题为“学习传感器和传感器网络”的讲座。在本文中,我们描述了课程模块及其在当地一所中学的五个班级和一个暑期外展项目的四个小组中的应用。最后,我们总结了评估结果,表明该计划产生了积极的结果。
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引用次数: 0
Session details: SIGITE Lightning Talk Session 1 会议详情:SIGITE闪电谈话会议1
Bill Paterson
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引用次数: 0
Experiential Learning Business/Industry and Education Wants and Needs 体验式学习商业/工业和教育的需求
Pub Date : 2015-09-29 DOI: 10.1145/2808006.2808012
Barbara Nicolai, D. Nevill, C. Hansen, Charles R. Winer
In our panel presentation, we discuss the relationships and necessary interactions between corporate entities, university faculty, and students as they relate to Experiential Learning (ExL) student experiences.
在我们的小组演讲中,我们讨论了企业实体、大学教师和学生之间的关系和必要的互动,因为它们与体验式学习(ExL)学生体验有关。
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引用次数: 0
Work in Progress: Improving the Performance of the Radial Basis Function Network 工作进展:改进径向基函数网络的性能
Pub Date : 2015-09-29 DOI: 10.1145/2808006.2808046
Alexander Strong, Timothy Gonzales, Logan Smith
This work investigates several novel variations of the Radial Basis Function Network with a view to determining whether accuracy can be maintained or improved in large dimensional classification problems without increasing amount of training data.
这项工作研究了径向基函数网络的几种新变体,以确定在不增加训练数据量的情况下,是否可以保持或提高大维度分类问题的准确性。
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引用次数: 1
Session details: SIGITE Lightning Talk Session 2 会议详情:SIGITE闪电会谈2
Baijian (Justin) Yang
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引用次数: 0
Lab on a Stick 实验棒
Pub Date : 2015-09-29 DOI: 10.1145/2808006.2808034
Peter Shipman, Ronny L. Bull
Lab on a Stick (LoaS) is a portable, modular, and scalable way to teach networking, systems administration, and computer security related material to students of all ages. The LoaS model empowers educators to develop customized virtual laboratory environments hosted on commonly available USB flash drives that are easily distributable. The students can complete various networking or computer security related exercises in a classroom environment, or take the entire laboratory home to continue work at their own pace. A portable, decentralized virtual environment has many benefits over centralized approaches which are illustrated through four different use cases that each employ one of two different variations of the LoaS model. Lab on a Stick has been shown to be useful for full semester classes in higher education, cybersecurity summer camps for junior high and high school students, as well as for students who would like to extend their learning beyond what is traditionally offered in the classroom. This paper details the key benefits provided by the Lab on a Stick model, and also includes performance information gathered after evaluating LoaS on various USB 2.0 and 3.0 flash drives.
Lab on a Stick (LoaS)是一种便携式、模块化和可伸缩的方法,用于向所有年龄段的学生教授网络、系统管理和计算机安全相关材料。LoaS模型使教育工作者能够在易于分发的通用USB闪存驱动器上开发定制的虚拟实验室环境。学生们可以在教室环境中完成各种网络或计算机安全相关的练习,或者把整个实验室带回家,按照自己的节奏继续工作。与集中式方法相比,可移植的、分散的虚拟环境有许多优点,这些优点通过四个不同的用例来说明,每个用例使用LoaS模型的两种不同变体中的一种。实验棒已经被证明对高等教育的整个学期课程,初中和高中学生的网络安全夏令营,以及那些想要扩展他们的学习超越传统课堂的学生都很有用。本文详细介绍了实验室在u盘模型上提供的主要优势,并包括在评估各种USB 2.0和3.0闪存驱动器上的LoaS后收集的性能信息。
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引用次数: 6
期刊
Proceedings of the 16th Annual Conference on Information Technology Education
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