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Proceedings of the Second (2015) ACM Conference on Learning @ Scale最新文献

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All It Takes Is One: Evidence for a Strategy for Seeding Large Scale Peer Learning Interactions 它所需要的只是一个:为大规模同伴学习互动播种策略的证据
Pub Date : 2015-03-14 DOI: 10.1145/2724660.2728698
Marti A. Hearst, A. Fox, Derrick Coetzee, Bjoern Hartmann
The results of a study of online peer learning suggests that it may be advantageous to automatically assign students to small peer learning groups based on how many students initially get answers to questions correct.
一项在线同侪学习的研究结果表明,根据最初答对问题的学生数量,自动将学生分配到同侪学习小组可能是有利的。
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引用次数: 1
Educational Evaluation in the PKU SPOC Course "Data Structures and Algorithms" 北京大学SPOC《数据结构与算法》课程教学评价
Pub Date : 2015-03-14 DOI: 10.1145/2724660.2728666
Ming Zhang, Jile Zhu, Yanzhen Zou, Hongfei Yan, Dan Hao, Chuxiong Liu
In order to learn the impact of MOOCs, we conducted a SPOC experiment on the course of Data Structures and Algorithms in Peking University. In this paper, we analyze student online activities, test scores, and two surveys using statistical methods (t-test, analysis of variance, correlation analysis and OLS regression) to understand what factors will foster improvements in student learning. We find that the "SPOC + Flipped" is a helpful mode to teach algorithm, time spent on the course and students' confidence had a positive impact on learning effect, and SPOC resource should be made full use of.
为了了解mooc的影响,我们对北京大学的《数据结构与算法》课程进行了SPOC实验。在本文中,我们使用统计方法(t检验、方差分析、相关分析和OLS回归)分析学生的在线活动、考试成绩和两次调查,以了解哪些因素会促进学生学习的改善。我们发现“SPOC +翻转”是一种有益的算法教学模式,在课程上花费的时间和学生的信心对学习效果有积极的影响,应该充分利用SPOC资源。
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引用次数: 16
Clustering-Based Active Learning for CPSGrader 基于聚类的CPSGrader主动学习
Pub Date : 2015-03-14 DOI: 10.1145/2724660.2728702
Garvit Juniwal, Sakshi Jain, Alexandre Donzé, S. Seshia
In this work, we propose and evaluate an active learning algorithm in context of CPSGrader, an automatic grading and feedback generation tool for laboratory-based courses in the area of cyber-physical systems. CPSGrader detects the presence of certain classes of mistakes using test benches that are generated in part via machine learning from solutions that have the fault and those that do not (positive and negative examples). We develop a clustering-based active learning technique that selects from a large database of unlabeled solutions, a small number of reference solutions for the expert to label that will be used as training data. The goal is to achieve better accuracy of fault identification with fewer reference solutions as compared to random selection. We demonstrate the effectiveness of our algorithm using data obtained from an on-campus laboratory-based course at UC Berkeley.
在这项工作中,我们提出并评估了CPSGrader背景下的主动学习算法,CPSGrader是一种用于网络物理系统领域实验室课程的自动评分和反馈生成工具。CPSGrader使用测试台来检测某些类型错误的存在,这些测试台部分是通过机器学习从有错误和没有错误的解决方案中生成的(正面和负面示例)。我们开发了一种基于聚类的主动学习技术,该技术从大量未标记的解决方案数据库中选择少量的参考解决方案供专家标记,这些解决方案将用作训练数据。与随机选择相比,目标是在较少的参考解的情况下实现更高的故障识别精度。我们使用从加州大学伯克利分校的一门校内实验室课程中获得的数据来证明我们算法的有效性。
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引用次数: 3
PeerStudio: Rapid Peer Feedback Emphasizes Revision and Improves Performance PeerStudio:快速同伴反馈强调修改和提高性能
Pub Date : 2015-03-14 DOI: 10.1145/2724660.2724670
Chinmay Kulkarni, Michael S. Bernstein, Scott R. Klemmer
Rapid feedback is a core component of mastery learning, but feedback on open-ended work requires days or weeks in most classes today. This paper introduces PeerStudio, an assessment platform that leverages the large number of students' peers in online classes to enable rapid feedback on in-progress work. Students submit their draft, give rubric-based feedback on two peers' drafts, and then receive peer feedback. Students can integrate the feedback and repeat this process as often as they desire. In MOOC deployments, the median student received feedback in just twenty minutes. Rapid feedback on in-progress work improves course outcomes: in a controlled experiment, students' final grades improved when feedback was delivered quickly, but not if delayed by 24 hours. More than 3,600 students have used PeerStudio in eight classes, both massive and in-person. This research demonstrates how large classes can leverage their scale to encourage mastery through rapid feedback and revision.
快速反馈是精通学习的核心组成部分,但在今天的大多数课程中,开放式作业的反馈需要几天或几周的时间。本文介绍了PeerStudio,这是一个评估平台,它利用在线课程中大量学生的同龄人来实现对正在进行的工作的快速反馈。学生提交他们的草稿,对两个同学的草稿给出基于规则的反馈,然后收到同学的反馈。学生可以整合反馈,并按照自己的意愿重复这个过程。在MOOC部署中,学生收到反馈的平均时间是20分钟。对正在进行的作业的快速反馈提高了课程效果:在一项对照实验中,当反馈快速传递时,学生的最终成绩会有所提高,但如果延迟24小时则不会。超过3600名学生在8堂课上使用了PeerStudio,包括大规模的和面对面的。这项研究证明了大班是如何利用他们的规模,通过快速的反馈和修改来鼓励学生精通的。
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引用次数: 178
Machine Learning for Learning at Scale 大规模学习的机器学习
Pub Date : 2015-03-14 DOI: 10.1145/2724660.2735205
Peter Norvig
There is great enthusiasm for the idea that massive amounts of data from online interactions of learners with material can lead to a rapid improvement cycle, driven by analysis of the data, experimentation, and intervention to do more of what works and less of what doesn't. This talk discusses techniques for working with massive amounts of data. Peter Norvig is a Director of Research at Google Inc. Previously he was head of Google's core search algorithms group, and of NASA Ames's Computational Sciences Division, making him NASA's senior computer scientist. He received the NASA Exceptional Achievement Award in 2001. He has taught at the University of Southern California and the University of California at Berkeley, from which he received a Ph.D. in 1986 and the distinguished alumni award in 2006. He was co-teacher of an Artificial Intelligence class that signed up 160,000 students, helping to kick off the current round of massive open online classes. His publications include the books Artificial Intelligence: A Modern Approach (the leading textbook in the field), Paradigms of AI Programming: Case Studies in Common Lisp, Verbmobil: A Translation System for Face-to-Face Dialog, and Intelligent Help Systems for UNIX. He is also the author of the Gettysburg Powerpoint Presentation and the world's longest palindromic sentence. He is a fellow of the AAAI, ACM, California Academy of Science and American Academy of Arts & Sciences.
学习者与材料在线互动的大量数据可以导致快速的改进周期,这一想法受到了极大的热情,通过分析数据、实验和干预来做更多有效的事情,减少无效的事情。这次演讲讨论了处理大量数据的技术。Peter Norvig是谷歌公司的研究主管。此前,他是谷歌核心搜索算法组的负责人,也是美国宇航局艾姆斯计算科学部的负责人,这使他成为美国宇航局的高级计算机科学家。他在2001年获得了美国国家航空航天局的杰出成就奖。他曾任教于南加州大学和加州大学伯克利分校,于1986年获得博士学位,并于2006年获得杰出校友奖。他是一个人工智能课程的联合老师,该课程有16万名学生报名,帮助开启了当前这一轮大规模的在线公开课程。他的著作包括《人工智能:一种现代方法》(该领域领先的教科书)、《人工智能编程范式:通用Lisp案例研究》、《vermobil:面对面对话的翻译系统》和《UNIX智能帮助系统》。他也是葛底斯堡ppt的作者,也是世界上最长的回文句子的作者。他是AAAI, ACM,加州科学院和美国艺术与科学学院的研究员。
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引用次数: 0
Learnersourcing of Complex Assessments 复杂评估的学习者资源
Pub Date : 2015-03-14 DOI: 10.1145/2724660.2728683
Piotr Mitros
We present results from a pilot study where students successfully created complex assessments for a MOOC in introductory electronics -- an area with a very large expert-novice gap. Previous work in learnersourcing found that learners can productively contribute through simple tasks. However, many course resources require a high level of expertise to create, and prior work fell short on tasks with a large expert-novice gap, such as textbook creation or concept tagging. Since these constitute a substantial portion of course creation costs, addressing this issue is prerequisite to substantially shifting MOOC economics through learnersourcing. This represents one of the first successes in learnersourcing with a large expert-novice gap. In the pilot, we reached out to 206 students (out of thousands who met eligibility criteria) who contributed 14 complex high-quality design problems. This results suggests a full cohort could contribute hundreds of problems. We achieved this through a four-pronged approach: (1) pre-selecting top learners (2) community feedback process (3) student mini-course in pedagogy (4) instructor review and involvement.
我们展示了一项试点研究的结果,在这项研究中,学生们成功地为入门电子课程的MOOC创建了复杂的评估,这是一个专家与新手之间存在很大差距的领域。之前关于learnersourcing的研究发现,学习者可以通过简单的任务做出富有成效的贡献。然而,许多课程资源需要高水平的专业知识来创建,并且先前的工作在专家与新手之间存在巨大差距的任务上表现不佳,例如教科书创建或概念标记。由于这些费用构成了课程创建成本的很大一部分,因此解决这一问题是通过学习者资源大幅改变MOOC经济的先决条件。这是在专家和新手之间存在巨大差距的情况下,学习者外包的首批成功案例之一。在试点中,我们接触了206名学生(从数千名符合资格标准的学生中),他们贡献了14个复杂的高质量设计问题。这一结果表明,一个完整的群体可能会造成数百个问题。我们通过四个方面的方法实现了这一目标:(1)预选最佳学习者(2)社区反馈过程(3)学生教育学迷你课程(4)教师评审和参与。
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引用次数: 23
Measuring and Maximizing the Effectiveness of Honor Codes in Online Courses 衡量和最大化在线课程中荣誉守则的有效性
Pub Date : 2015-03-14 DOI: 10.1145/2724660.2728663
Henry Corrigan-Gibbs, Nakull Gupta, Curtis G. Northcutt, Edward Cutrell, W. Thies
We measure the effectiveness of a traditional honor code at deterring cheating in an online examination, and we compare it to that of a stern warning. Through experimental evaluation in a 409-student online course, we find that a pre-task warning leads to a significant decrease in the rate of cheating while an honor code has a smaller (non-significant) effect. Unlike much prior work, we measure the rate of cheating directly and we do not rely on potentially inaccurate post-examination surveys. Our findings demonstrate that replacing traditional honor codes with warnings could be a simple and effective way to deter cheating in online courses.
我们衡量了传统荣誉准则在阻止在线考试作弊方面的有效性,并将其与严厉警告进行了比较。通过对409名学生在线课程的实验评估,我们发现任务前警告导致作弊率显著降低,而荣誉守则的影响较小(不显著)。与之前的许多工作不同,我们直接测量作弊率,我们不依赖于可能不准确的考试后调查。我们的研究结果表明,用警告取代传统的荣誉守则可能是一种简单有效的阻止在线课程作弊的方法。
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引用次数: 17
An Automated Grading/Feedback System for 3-View Engineering Drawings using RANSAC 使用RANSAC的3视图工程图纸自动分级/反馈系统
Pub Date : 2015-03-14 DOI: 10.1145/2724660.2724682
Y. Kwon, Sara McMains
We propose a novel automated grading system that can compare two multiview engineering drawings consisting of three views that may have allowable translations, scales, and offsets, and can recognize frequent error types as well as individual drawing errors. We show that translation, scale, and offset-invariant comparison can be conducted by estimating the affine transformation for each individual view within drawings. Our system directly aims to evaluate students' skills creating multiview engineering drawings. Since it is important for our students to be familiar with widely used software such as AutoCAD, our system does not require a separate interface or environment, but directly grades the saved DWG/DXF files from AutoCAD. We show the efficacy of the proposed algorithm by comparing its results with human grading. Beyond the advantages of convenience and accuracy, based on our data set of students' answers, we can analyze the common errors of the class as a whole using our system.
我们提出了一种新的自动分级系统,可以比较由三个视图组成的两个多视图工程图,这些视图可能具有允许的平移,比例尺和偏移量,并且可以识别常见的错误类型以及单个绘图错误。我们表明,平移、比例和偏移不变的比较可以通过估计图纸中每个单独视图的仿射变换来进行。我们的系统直接旨在评估学生绘制多视图工程图的技能。由于熟悉AutoCAD等广泛使用的软件对我们的学生很重要,所以我们的系统不需要单独的界面或环境,而是直接从AutoCAD中保存DWG/DXF文件进行评分。我们通过将其结果与人工评分结果进行比较来证明所提出算法的有效性。除了方便和准确的优点之外,基于我们的学生答案数据集,我们可以使用我们的系统分析整个班级的常见错误。
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引用次数: 10
M-CAFE: Managing MOOC Student Feedback with Collaborative Filtering M-CAFE:用协同过滤管理MOOC学生反馈
Pub Date : 2015-03-14 DOI: 10.1145/2724660.2728681
Mo Zhou, A. Cliff, Allen Huang, S. Krishnan, Brandie Nonnecke, Kanji Uchino, Samuelson Joseph, A. Fox, Ken Goldberg
Ongoing student feedback on course content and assignments can be valuable for MOOC instructors in the absence of face-to-face-interaction. To collect ongoing feedback and scalably identify valuable suggestions, we built the MOOC Collaborative Assessment and Feedback Engine (M-CAFE). This mobile platform allows MOOC students to numerically assess the course, their own performance, and provide textual suggestions about how the course could be improved on a weekly basis. M-CAFE allows students to visualize how they compare with their peers and read and evaluate what others have suggested, providing peer-to-peer collaborative filtering. We evaluate M-CAFE based on data from two EdX MOOCs.
在缺乏面对面交流的情况下,学生对课程内容和作业的持续反馈对MOOC教师来说很有价值。为了收集持续的反馈并可扩展地识别有价值的建议,我们建立了MOOC协作评估和反馈引擎(M-CAFE)。这个移动平台允许MOOC学生对课程和他们自己的表现进行数字评估,并提供关于如何改进课程的文本建议。M-CAFE允许学生可视化他们与同龄人的比较,并阅读和评估其他人的建议,提供点对点的协作过滤。我们基于两个EdX mooc的数据来评估M-CAFE。
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引用次数: 7
Learn With Friends: The Effects of Student Face-to-Face Collaborations on Massive Open Online Course Activities 与朋友一起学习:学生面对面合作对大规模开放在线课程活动的影响
Pub Date : 2015-03-14 DOI: 10.1145/2724660.2728667
Christopher A. Brooks, Caren M. Stalburg, Tawanna R. Dillahunt, L. Robert
This work investigates whether enrolling in a Massive Open Online Course (MOOC) with friends or colleagues can improve a learner's performance and social interaction during the course. Our results suggest that signing up for a MOOC with peers correlates positively with the rate of course completion, level of achievement, and discussion forum usage. Further analysis seems to suggest that a learner's interaction with their friends compliments a MOOC by acting as a form of self-blended learning.
这项研究调查了与朋友或同事一起参加大规模开放在线课程(MOOC)是否能提高学习者在课程中的表现和社交互动。我们的研究结果表明,与同龄人一起报名参加MOOC与课程完成率、成绩水平和论坛使用率呈正相关。进一步的分析似乎表明,学习者与朋友的互动作为一种自我混合学习的形式,对MOOC进行了赞美。
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引用次数: 9
期刊
Proceedings of the Second (2015) ACM Conference on Learning @ Scale
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