Proceedings of the first ACM conference on Learning @ scale conference最新文献_第2页

Code hunt: gamifying teaching and learning of computer science at scale 代码搜索:大规模计算机科学的游戏化教学

Proceedings of the first ACM conference on Learning @ scale conference Pub Date : 2014-03-04 DOI: 10.1145/2556325.2567870

N. Tillmann, J. D. Halleux, Tao Xie, J. Bishop

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引用次数: 33

Teacher usage behaviors within an online open educational resource repository 在线开放教育资源库中的教师使用行为

Proceedings of the first ACM conference on Learning @ scale conference Pub Date : 2014-03-04 DOI: 10.1145/2556325.2567875

Jennifer Sabourin, Lucy Kosturko, Scott W. McQuiggan

引用次数: 2

Due dates in MOOCs: does stricter mean better? mooc的截止日期:更严格意味着更好吗?

Proceedings of the first ACM conference on Learning @ scale conference Pub Date : 2014-03-04 DOI: 10.1145/2556325.2567877

Sergiy O. Nesterko, Daniel T. Seaton, J. Reich, Joseph McIntyre, Qiuyi Han, Isaac L. Chuang, Andrew D. Ho

引用次数: 12

ForumDash: analyzing online discussion forums ForumDash:在线论坛分析

Proceedings of the first ACM conference on Learning @ scale conference Pub Date : 2014-03-04 DOI: 10.1145/2556325.2567848

Jacquelin A. Speck, Eugene Gualtieri, G. Naik, Thach Nguyen, Kevin K. F. Cheung, L. Alexander, David E. Fenske

引用次数: 8

Chatrooms in MOOCs: all talk and no action mooc的聊天室:只说不做

Proceedings of the first ACM conference on Learning @ scale conference Pub Date : 2014-03-04 DOI: 10.1145/2556325.2566242

Derrick Coetzee, A. Fox, Marti A. Hearst, Bjoern Hartmann

引用次数: 64

Social factors that contribute to attrition in MOOCs 导致mooc流失的社会因素

Proceedings of the first ACM conference on Learning @ scale conference Pub Date : 2014-03-04 DOI: 10.1145/2556325.2567879

C. Rosé, R. Carlson, Diyi Yang, Miaomiao Wen, L. Resnick, Pam Goldman, Jennifer Sherer

引用次数: 136

Scaling short-answer grading by combining peer assessment with algorithmic scoring 通过将同行评估与算法评分相结合来扩展简答评分

Proceedings of the first ACM conference on Learning @ scale conference Pub Date : 2014-03-04 DOI: 10.1145/2556325.2566238

Chinmay Kulkarni, R. Socher, Michael S. Bernstein, Scott R. Klemmer

{"title":"Scaling short-answer grading by combining peer assessment with algorithmic scoring","authors":"Chinmay Kulkarni, R. Socher, Michael S. Bernstein, Scott R. Klemmer","doi":"10.1145/2556325.2566238","DOIUrl":"https://doi.org/10.1145/2556325.2566238","url":null,"abstract":"Peer assessment helps students reflect and exposes them to different ideas. It scales assessment and allows large online classes to use open-ended assignments. However, it requires students to spend significant time grading. How can we lower this grading burden while maintaining quality? This paper integrates peer and machine grading to preserve the robustness of peer assessment and lower grading burden. In the identify-verify pattern, a grading algorithm first predicts a student grade and estimates confidence, which is used to estimate the number of peer raters required. Peers then identify key features of the answer using a rubric. Finally, other peers verify whether these feature labels were accurately applied. This pattern adjusts the number of peers that evaluate an answer based on algorithmic confidence and peer agreement. We evaluated this pattern with 1370 students in a large, online design class. With only 54% of the student grading time, the identify-verify pattern yields 80-90% of the accuracy obtained by taking the median of three peer scores, and provides more detailed feedback. A second experiment found that verification dramatically improves accuracy with more raters, with a 20% gain over the peer-median with four raters. However, verification also leads to lower initial trust in the grading system. The identify-verify pattern provides an example of how peer work and machine learning can combine to improve the learning experience.","PeriodicalId":20830,"journal":{"name":"Proceedings of the first ACM conference on Learning @ scale conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75750471","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: 96

Reducing non-response bias with survey reweighting: applications for online learning researchers 通过调查重新加权减少非反应偏差:在线学习研究人员的应用

Proceedings of the first ACM conference on Learning @ scale conference Pub Date : 2014-03-04 DOI: 10.1145/2556325.2567850

René F. Kizilcec

引用次数: 4

Corporate learning at scale: lessons from a large online course at google 大规模企业学习:来自谷歌大型在线课程的经验教训

Proceedings of the first ACM conference on Learning @ scale conference Pub Date : 2014-03-04 DOI: 10.1145/2556325.2567874

A. Asuncion, J. D. Haan, M. Mohri, Kayur Patel, Afshin Rostamizadeh, Umar Syed, Lauren Wong

引用次数: 4

Self-evaluation in advanced power searching and mapping with google MOOCs 基于google mooc的高级电力搜索和地图的自我评价

Proceedings of the first ACM conference on Learning @ scale conference Pub Date : 2014-03-04 DOI: 10.1145/2556325.2566241

Julia Wilkowski, D. Russell, Amit Deutsch

引用次数: 19