Proceedings of the Third (2016) ACM Conference on Learning @ Scale最新文献_第2页

Improving the Peer Assessment Experience on MOOC Platforms 改善MOOC平台上的同行评议体验

Proceedings of the Third (2016) ACM Conference on Learning @ Scale Pub Date : 2016-04-25 DOI: 10.1145/2876034.2876043

T. Staubitz, Dominic Petrick, Matthias Bauer, Jan Renz, C. Meinel

引用次数: 60

The Future of Learning 学习的未来

Proceedings of the Third (2016) ACM Conference on Learning @ Scale Pub Date : 2016-04-25 DOI: 10.1145/2876034.2876053

Sugata Mitra

引用次数: 34

Designing for Open Learning: Design Principles and Scalability Affordances in Practice 开放学习的设计:实践中的设计原则和可扩展性

Proceedings of the Third (2016) ACM Conference on Learning @ Scale Pub Date : 2016-04-25 DOI: 10.1145/2876034.2893426

O. Firssova, F. Brouns, M. Kalz

引用次数: 3

A Data-Driven Approach for Inferring Student Proficiency from Game Activity Logs 从游戏活动日志推断学生熟练程度的数据驱动方法

Proceedings of the Third (2016) ACM Conference on Learning @ Scale Pub Date : 2016-04-25 DOI: 10.1145/2876034.2876038

M. Falakmasir, José P. González-Brenes, Geoffrey J. Gordon, K. DiCerbo

引用次数: 22

How Mastery Learning Works at Scale 精通学习是如何大规模运作的

Proceedings of the Third (2016) ACM Conference on Learning @ Scale Pub Date : 2016-04-25 DOI: 10.1145/2876034.2876039

Steven Ritter, M. Yudelson, Stephen E. Fancsali, Susan R. Berman

{"title":"How Mastery Learning Works at Scale","authors":"Steven Ritter, M. Yudelson, Stephen E. Fancsali, Susan R. Berman","doi":"10.1145/2876034.2876039","DOIUrl":"https://doi.org/10.1145/2876034.2876039","url":null,"abstract":"Nearly every adaptive learning system aims to present students with materials personalized to their level of understanding (Enyedy, 2014). Typically, such adaptation follows some form of mastery learning (Bloom, 1968), in which students are asked to master one topic before proceeding to the next topic. Mastery learning programs have a long history of success (Guskey and Gates, 1986; Kulik, Kulik & Bangert-Drowns, 1990) and have been shown to be superior to alternative instructional approaches. Although there is evidence for the effectiveness of mastery learning when it is well supported by teachers, mastery learning's effectiveness is crucially dependent on the ability and willingness of teachers to implement it properly. In particular, school environments impose time constraints and set goals for curriculum coverage that may encourage teachers to deviate from mastery-based instruction. In this paper we examine mastery learning as implemented in Carnegie Learning's Cognitive Tutor. Like in all real-world systems, teachers and students have the ability to violate mastery learning guidance. We investigate patterns associated with violating and following mastery learning over the course of the full school year at the class and student level. We find that violations of mastery learning are associated with poorer student performance, especially among struggling students, and that this result is likely attributable to such violations of mastery learning.","PeriodicalId":20739,"journal":{"name":"Proceedings of the Third (2016) ACM Conference on Learning @ Scale","volume":"47 186 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83278900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 63

Designing Videos with Pedagogical Strategies: Online Students' Perceptions of Their Effectiveness 用教学策略设计视频:在线学生对其有效性的看法

Proceedings of the Third (2016) ACM Conference on Learning @ Scale Pub Date : 2016-04-25 DOI: 10.1145/2876034.2893391

Chaohua Ou, Ashok K. Goel, David A. Joyner, Daniel F. Haynes

引用次数: 16

A Preliminary Look at MOOC-associated Facebook Groups: Prevalence, Geographic Representation, and Homophily 与mooc相关的Facebook群组的初步研究:流行程度、地域代表性和同质性

Proceedings of the Third (2016) ACM Conference on Learning @ Scale Pub Date : 2016-04-25 DOI: 10.1145/2876034.2893415

Anna Kasunic, Jessica Hammer, R. Kraut, M. Massimi, A. Ogan

引用次数: 4

Structured Knowledge Tracing Models for Student Assessment on Coursera 面向Coursera学生评估的结构化知识追踪模型

Proceedings of the Third (2016) ACM Conference on Learning @ Scale Pub Date : 2016-04-25 DOI: 10.1145/2876034.2893416

Zhuo Wang, Jile Zhu, Xiang Li, Zhiting Hu, Ming Zhang

引用次数: 28

Brain Points: A Deeper Look at a Growth Mindset Incentive Structure for an Educational Game 《Brain Points:教育类游戏的成长心态激励结构

Proceedings of the Third (2016) ACM Conference on Learning @ Scale Pub Date : 2016-04-25 DOI: 10.1145/2876034.2876040

Eleanor O'Rourke, E. Peach, C. Dweck, Zoran Popovic

{"title":"Brain Points: A Deeper Look at a Growth Mindset Incentive Structure for an Educational Game","authors":"Eleanor O'Rourke, E. Peach, C. Dweck, Zoran Popovic","doi":"10.1145/2876034.2876040","DOIUrl":"https://doi.org/10.1145/2876034.2876040","url":null,"abstract":"Student retention is a central challenge in systems for learning at scale. It has been argued that educational video games could improve student retention by providing engaging experiences and informing the design of other online learning environments. However, educational games are not uniformly effective. Our recent research shows that player retention can be increased by using a brain points incentive structure that rewards behaviors associated with growth mindset, or the belief that intelligence can grow. In this paper, we expand on our prior work by providing new insights into how growth mindset behaviors can be effectively promoted in the educational game Refraction. We present results from an online study of 25,000 children who were exposed to five different versions of the brain points intervention. We find that growth mindset animations cause a large number of players to quit, while brain points encourage persistence. Most importantly, we find that awarding brain points randomly is ineffective; the incentive structure is successful specifically because it rewards desirable growth mindset behaviors. These findings have important implications that can support the future generalization of the brain points intervention to new educational contexts.","PeriodicalId":20739,"journal":{"name":"Proceedings of the Third (2016) ACM Conference on Learning @ Scale","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83670044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 27

Explaining Student Behavior at Scale: The Influence of Video Complexity on Student Dwelling Time 在尺度上解释学生行为:视频复杂性对学生停留时间的影响

Proceedings of the Third (2016) ACM Conference on Learning @ Scale Pub Date : 2016-04-25 DOI: 10.1145/2876034.2876051

F. V. D. Sluis, Jasper Ginn, T. Zee

{"title":"Explaining Student Behavior at Scale: The Influence of Video Complexity on Student Dwelling Time","authors":"F. V. D. Sluis, Jasper Ginn, T. Zee","doi":"10.1145/2876034.2876051","DOIUrl":"https://doi.org/10.1145/2876034.2876051","url":null,"abstract":"Understanding why and how students interact with educational videos is essential to further improve the quality of MOOCs. In this paper, we look at the complexity of videos to explain two related aspects of student behavior: the dwelling time (how much time students spend watching a video) and the dwelling rate (how much of the video they actually see). Building on a strong tradition of psycholinguistics, we formalize a definition for information complexity in videos. Furthermore, building on recent advancements in time-on-task measures we formalize dwelling time and dwelling rate based on click-stream trace data. The resulting computational model of video complexity explains 22.44% of the variance in the dwelling rate for students that finish watching a paragraph of a video. Video complexity and student dwelling show a polynomial relationship, where both low and high complexity increases dwelling. These results indicate why students spend more time watching (and possibly contemplating about) a video. Furthermore, they show that even fairly straightforward proxies of student behavior such as dwelling can already have multiple interpretations; illustrating the challenge of sense-making from learning analytics.","PeriodicalId":20739,"journal":{"name":"Proceedings of the Third (2016) ACM Conference on Learning @ Scale","volume":"96 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85885567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 42