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

Who You Are or What You Do: Comparing the Predictive Power of Demographics vs. Activity Patterns in Massive Open Online Courses (MOOCs) 你是谁或你在做什么:比较大规模在线开放课程(MOOCs)中人口统计数据与活动模式的预测能力

Proceedings of the Second (2015) ACM Conference on Learning @ Scale Pub Date : 2015-03-14 DOI: 10.1145/2724660.2728668

Christopher A. Brooks, Craig D. S. Thompson, Stephanie D. Teasley

引用次数: 29

Are You Listening?: Social Roles and Perceived Value of Statements in Online Learning Communities 你在听吗?在线学习社区中陈述的社会角色和感知价值

Proceedings of the Second (2015) ACM Conference on Learning @ Scale Pub Date : 2015-03-14 DOI: 10.1145/2724660.2728697

R. Shillair, Rick Wash

引用次数: 0

Practical Measures of Learning Behaviors 学习行为的实际测量

Proceedings of the Second (2015) ACM Conference on Learning @ Scale Pub Date : 2015-03-14 DOI: 10.1145/2724660.2728685

Andrew E. Krumm, C. D'Angelo, T. Podkul, Mingyu Feng, H. Yamada, Rachel Beattie, Heather Hough, Christopher A. Thorn

引用次数: 4

Learning is Not a Spectator Sport: Doing is Better than Watching for Learning from a MOOC 学习不是一项观赏性的运动:从MOOC上学习，做比看更好

Proceedings of the Second (2015) ACM Conference on Learning @ Scale Pub Date : 2015-03-14 DOI: 10.1145/2724660.2724681

K. Koedinger, Ji Hee Kim, J. Z. Jia, Elizabeth Mclaughlin, Norman L. Bier

引用次数: 231

Cultural Relevance in MOOCs: Asking About Socioeconomic Context MOOCs中的文化关联:询问社会经济背景

Proceedings of the Second (2015) ACM Conference on Learning @ Scale Pub Date : 2015-03-14 DOI: 10.1145/2724660.2728700

Anna Kasunic, Jessica Hammer, A. Ogan

引用次数: 5

Game Theory based Peer Grading Mechanisms for MOOCs 基于博弈论的mooc同伴评分机制

Proceedings of the Second (2015) ACM Conference on Learning @ Scale Pub Date : 2015-03-14 DOI: 10.1145/2724660.2728676

William Wu, C. Daskalakis, N. Kaashoek, Christos Tzamos, S. Weinberg

{"title":"Game Theory based Peer Grading Mechanisms for MOOCs","authors":"William Wu, C. Daskalakis, N. Kaashoek, Christos Tzamos, S. Weinberg","doi":"10.1145/2724660.2728676","DOIUrl":"https://doi.org/10.1145/2724660.2728676","url":null,"abstract":"An efficient peer grading mechanism is proposed for grading the multitude of assignments in online courses. This novel approach is based on game theory and mechanism design. A set of assumptions and a mathematical model is ratified to simulate the dominant strategy behavior of students in a given mechanism. A benchmark function accounting for grade accuracy and workload is established to quantitatively compare effectiveness and scalability of various mechanisms. After multiple iterations of mechanisms under increasingly realistic assumptions, three are proposed: Calibration, Improved Calibration, and Deduction. The Calibration mechanism performs as predicted by game theory when tested in an online crowd-sourced experiment, but fails when students are assumed to communicate. The Improved Calibration mechanism addresses this assumption, but at the cost of more effort spent grading. The Deduction mechanism performs relatively well in the benchmark, outperforming the Calibration, Improved Calibration, traditional automated, and traditional peer grading systems. The mathematical model and benchmark opens the way for future derivative works to be performed and compared.","PeriodicalId":20664,"journal":{"name":"Proceedings of the Second (2015) ACM Conference on Learning @ Scale","volume":"92 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81638946","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: 13

Do Numeric Ratings Impact Peer Reviewers? 数字评级会影响同行评审吗?

Proceedings of the Second (2015) ACM Conference on Learning @ Scale Pub Date : 2015-03-14 DOI: 10.1145/2724660.2728693

Catherine M. Hicks, C. Fraser, Purvi Desai, Scott R. Klemmer

引用次数: 6

Blended Learning in Indian Colleges with Massively Empowered Classroom 印度大学与大规模授权课堂的混合学习

Proceedings of the Second (2015) ACM Conference on Learning @ Scale Pub Date : 2015-03-14 DOI: 10.1145/2724660.2724666

Edward Cutrell, Jacki O 'neill, S. Bala, B. Nitish, A. Cross, Nakull Gupta, Viraj Kumar, W. Thies, Microsoft Research

引用次数: 25

Probabilistic Use Cases: Discovering Behavioral Patterns for Predicting Certification 概率用例:发现用于预测认证的行为模式

Proceedings of the Second (2015) ACM Conference on Learning @ Scale Pub Date : 2015-03-14 DOI: 10.1145/2724660.2724662

Cody A. Coleman, Daniel T. Seaton, Isaac L. Chuang

{"title":"Probabilistic Use Cases: Discovering Behavioral Patterns for Predicting Certification","authors":"Cody A. Coleman, Daniel T. Seaton, Isaac L. Chuang","doi":"10.1145/2724660.2724662","DOIUrl":"https://doi.org/10.1145/2724660.2724662","url":null,"abstract":"Advances in open-online education have led to a dramatic increase in the size, diversity, and traceability of learner populations, offering tremendous opportunities to study detailed learning behavior of users around the world. This paper adapts the topic modeling approach of Latent Dirichlet Allocation (LDA) to uncover behavioral structure from student logs in a MITx Massive Open Online Course, 8.02x: Electricity and Magnetism. LDA is typically found in the field of natural language processing, where it identifies the latent topic structure within a collection of documents. However, this framework can be adapted for analysis of user-behavioral patterns by considering user interactions with courseware as a ``bag of interactions'' equivalent to the ``bag of words'' model found in topic modeling. By employing this representation, LDA forms probabilistic use cases that clusters students based on their behavior. Through the probability distributions associated with each use case, this approach provides an interpretable representation of user access patterns, while reducing the dimensionality of the data and improving accuracy. Using only the first week of logs, we can predict whether or not a student will earn a certificate with 0.81 ± 0.01 cross-validation accuracy. Thus, the method presented in this paper is a powerful tool in understanding user behavior and predicting outcomes.","PeriodicalId":20664,"journal":{"name":"Proceedings of the Second (2015) ACM Conference on Learning @ Scale","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83605809","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: 56

Learning Experiments Using AB Testing at Scale 大规模使用AB测试学习实验

Proceedings of the Second (2015) ACM Conference on Learning @ Scale Pub Date : 2015-03-14 DOI: 10.1145/2724660.2728703

Christopher Chudzicki, David E. Pritchard, Zhongzhou Chen

引用次数: 8