Proceedings of the Second (2015) ACM Conference on Learning @ Scale最新文献_第2页

Evaluating the Relationship Between Course Structure, Learner Activity, and Perceived Value of Online Courses 评估课程结构、学习者活动和在线课程感知价值之间的关系

Proceedings of the Second (2015) ACM Conference on Learning @ Scale Pub Date : 2015-03-14 DOI: 10.1145/2724660.2728699

Ido Roll, Leah P. Macfadyen, Debra Sandilands

引用次数: 3

Understanding Learners' General Perception Towards Learning with MOOC Classmates: An Exploratory Study 了解学习者对与MOOC同学一起学习的普遍认知:一项探索性研究

Proceedings of the Second (2015) ACM Conference on Learning @ Scale Pub Date : 2015-03-14 DOI: 10.1145/2724660.2728680

Soon Hau Chua, Juho Kim, T. K. Monserrat, Shengdong Zhao

引用次数: 3

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

Proceedings of the Second (2015) ACM Conference on Learning @ Scale Pub Date : 2015-03-14 DOI: 10.1145/2724660.2728698

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

引用次数: 1

TELLab: An Experiential Learning Tool for Psychology TELLab:心理学的体验式学习工具

Proceedings of the Second (2015) ACM Conference on Learning @ Scale Pub Date : 2015-03-14 DOI: 10.1145/2724660.2728678

Na Li, Krzysztof Z Gajos, K. Nakayama, Ryan D. Enos

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

Proceedings of the Second (2015) ACM Conference on Learning @ Scale Pub Date : 2015-03-14 DOI: 10.1145/2724660.2728666

Ming Zhang, Jile Zhu, Yanzhen Zou, Hongfei Yan, Dan Hao, Chuxiong Liu

引用次数: 16

Exploring the Effect of Confusion in Discussion Forums of Massive Open Online Courses 大规模网络公开课论坛混乱效应探析

Proceedings of the Second (2015) ACM Conference on Learning @ Scale Pub Date : 2015-03-14 DOI: 10.1145/2724660.2724677

Diyi Yang, Miaomiao Wen, I. Howley, R. Kraut, C. Rosé

引用次数: 106

Addressing Common Analytic Challenges to Randomized Experiments in MOOCs: Attrition and Zero-Inflation 解决mooc随机实验的常见分析挑战:损耗与零膨胀

Proceedings of the Second (2015) ACM Conference on Learning @ Scale Pub Date : 2015-03-14 DOI: 10.1145/2724660.2724669

Anne Lamb, Jascha Smilack, Andrew D. Ho, J. Reich

{"title":"Addressing Common Analytic Challenges to Randomized Experiments in MOOCs: Attrition and Zero-Inflation","authors":"Anne Lamb, Jascha Smilack, Andrew D. Ho, J. Reich","doi":"10.1145/2724660.2724669","DOIUrl":"https://doi.org/10.1145/2724660.2724669","url":null,"abstract":"Massive open online course (MOOC) platforms increasingly allow easily implemented randomized experiments. The heterogeneity of MOOC students, however, leads to two methodological obstacles in analyzing interventions to increase engagement. (1) Many MOOC participation metrics have distributions with substantial positive skew from highly active users as well as zero-inflation from high attrition. (2) High attrition means that in some experimental designs, most users assigned to the treatment never receive it; analyses that do not consider attrition result in \"intent-to-treat\" (ITT) estimates that underestimate the true effects of interventions. We address these challenges in analyzing an intervention to improve forum participation in the 2014 JusticeX course offered on the edX MOOC platform. We compare the results of four ITT models (OLS, logistic, quantile, and zero-inflated negative binomial regressions) and three \"treatment-on-treated\" (TOT) models (Wald estimator, 2SLS with a second stage logistic model, and instrumental variables quantile regression). A combination of logistic, quantile, and zero-inflated negative binomial regressions provide the most comprehensive description of the ITT effects. TOT methods then adjust the ITT underestimates. Substantively, we demonstrate that self-assessment questions about forum participation encourage more students to engage in forums and increases the participation of already active students.","PeriodicalId":20664,"journal":{"name":"Proceedings of the Second (2015) ACM Conference on Learning @ Scale","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81169041","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: 28

Problems Before Solutions: Automated Problem Clarification at Scale 解决方案之前的问题:大规模的自动问题澄清

Proceedings of the Second (2015) ACM Conference on Learning @ Scale Pub Date : 2015-03-14 DOI: 10.1145/2724660.2724679

S. Basu, A. Wu, Brian Hou, John DeNero

引用次数: 21

Bayesian Ordinal Peer Grading 贝叶斯有序同伴分级

Proceedings of the Second (2015) ACM Conference on Learning @ Scale Pub Date : 2015-03-14 DOI: 10.1145/2724660.2724678

Karthik Raman, T. Joachims

引用次数: 34

BayesRank: A Bayesian Approach to Ranked Peer Grading BayesRank:一种贝叶斯方法来排名同伴评分

Proceedings of the Second (2015) ACM Conference on Learning @ Scale Pub Date : 2015-03-14 DOI: 10.1145/2724660.2724672

Andrew E. Waters, David Tinapple, Richard Baraniuk

{"title":"BayesRank: A Bayesian Approach to Ranked Peer Grading","authors":"Andrew E. Waters, David Tinapple, Richard Baraniuk","doi":"10.1145/2724660.2724672","DOIUrl":"https://doi.org/10.1145/2724660.2724672","url":null,"abstract":"Advances in online and computer supported education afford exciting opportunities to revolutionize the classroom, while also presenting a number of new challenges not faced in traditional educational settings. Foremost among these challenges is the problem of accurately and efficiently evaluating learner work as the class size grows, which is directly related to the larger goal of providing quality, timely, and actionable formative feedback. Recently there has been a surge in interest in using peer grading methods coupled with machine learning to accurately and fairly evaluate learner work while alleviating the instructor bottleneck and grading overload. Prior work in peer grading almost exclusively focuses on numerically scored grades -- either real-valued or ordinal. In this work, we consider the implications of peer ranking in which learners rank a small subset of peer work from strongest to weakest, and propose new types of computational analyses that can be applied to this ranking data. We adopt a Bayesian approach to the ranked peer grading problem and develop a novel model and method for utilizing ranked peer-grading data. We additionally develop a novel procedure for adaptively identifying which work should be ranked by particular peers in order to dynamically resolve ambiguity in the data and rapidly resolve a clearer picture of learner performance. We showcase our results on both synthetic and several real-world educational datasets.","PeriodicalId":20664,"journal":{"name":"Proceedings of the Second (2015) ACM Conference on Learning @ Scale","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85998028","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: 19