Proceedings of the Fifth Annual ACM Conference on Learning at Scale最新文献_第2页

Squeezing the limeade: policies and workflows for scalable online degrees 榨橙汁:可扩展在线学位的政策和工作流程

Proceedings of the Fifth Annual ACM Conference on Learning at Scale Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231649

David A. Joyner

引用次数: 21

How much randomization is needed to deter collaborative cheating on asynchronous exams? 需要多少随机性才能阻止异步考试中的协作作弊?

Proceedings of the Fifth Annual ACM Conference on Learning at Scale Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231664

Binglin Chen, Matthew West, C. Zilles

{"title":"How much randomization is needed to deter collaborative cheating on asynchronous exams?","authors":"Binglin Chen, Matthew West, C. Zilles","doi":"10.1145/3231644.3231664","DOIUrl":"https://doi.org/10.1145/3231644.3231664","url":null,"abstract":"This paper investigates randomization on asynchronous exams as a defense against collaborative cheating. Asynchronous exams are those for which students take the exam at different times, potentially across a multi-day exam period. Collaborative cheating occurs when one student (the information producer) takes the exam early and passes information about the exam to other students (the information consumers) that are taking the exam later. Using a dataset of computerized exam and homework problems in a single course with 425 students, we identified 5.5% of students (on average) as information consumers by their disproportionate studying of problems that were on the exam. These information consumers (\"cheaters\") had a significant advantage (13 percentage points on average) when every student was given the same exam problem (even when the parameters are randomized for each student), but that advantage dropped to almost negligible levels (2--3 percentage points) when students were given a random problem from a pool of two or four problems. We conclude that randomization with pools of four (or even three) problems, which also contain randomized parameters, is an effective mitigation for collaborative cheating. Our analysis suggests that this mitigation is in part explained by cheating students having less complete information about larger pools.","PeriodicalId":20634,"journal":{"name":"Proceedings of the Fifth Annual ACM Conference on Learning at Scale","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87612934","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

Accelerated apprenticeship: teaching data science problem solving skills at scale 加速学徒:大规模教授数据科学问题解决技能

Proceedings of the Fifth Annual ACM Conference on Learning at Scale Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231697

Lujie Chen, A. Dubrawski

引用次数: 1

Designing a learning analytics dashboard for twitter-facilitated teaching 为twitter辅助教学设计一个学习分析仪表板

Proceedings of the Fifth Annual ACM Conference on Learning at Scale Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231704

A. Gruzd, Nadia Conroy

引用次数: 4

From online learning to offline action: using MOOCs for job-embedded teacher professional development 从线上学习到线下行动:利用mooc促进职业嵌入式教师专业发展

Proceedings of the Fifth Annual ACM Conference on Learning at Scale Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231674

Alyssa Napier, Elizabeth Huttner-Loan, J. Reich

引用次数: 4

Combining adaptivity with progression ordering for intelligent tutoring systems 结合自适应与进度排序的智能辅导系统

Proceedings of the Fifth Annual ACM Conference on Learning at Scale Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231672

Tong Mu, Shuhan Wang, Erik Andersen, E. Brunskill

引用次数: 16

Docent Docent

Proceedings of the Fifth Annual ACM Conference on Learning at Scale Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231646

Vineet Pandey, Justine W. Debelius, Embriette R. Hyde, T. Kosciólek, Rob Knight, Scott R. Klemmer

引用次数: 11

Team based assignments in MOOCs: results and observations mooc中基于团队的作业:结果和观察

Proceedings of the Fifth Annual ACM Conference on Learning at Scale Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231705

T. Staubitz, C. Meinel

引用次数: 13

Replicating MOOC predictive models at scale 大规模复制MOOC预测模型

Proceedings of the Fifth Annual ACM Conference on Learning at Scale Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231656

Josh Gardner, Christopher A. Brooks, J. M. Andres, R. Baker

{"title":"Replicating MOOC predictive models at scale","authors":"Josh Gardner, Christopher A. Brooks, J. M. Andres, R. Baker","doi":"10.1145/3231644.3231656","DOIUrl":"https://doi.org/10.1145/3231644.3231656","url":null,"abstract":"We present a case study in predictive model replication for student dropout in Massive Open Online Courses (MOOCs) using a large and diverse dataset (133 sessions of 28 unique courses offered by two institutions). This experiment was run on the MOOC Replication Framework (MORF), which makes it feasible to fully replicate complex machine learned models, from raw data to model evaluation. We provide an overview of the MORF platform architecture and functionality, and demonstrate its use through a case study. In this replication of [41], we contextualize and evaluate the results of the previous work using statistical tests and a more effective model evaluation scheme. We find that only some of the original findings replicate across this larger and more diverse sample of MOOCs, with others replicating significantly in the opposite direction. Our analysis also reveals results which are highly relevant to the prediction task which were not reported in the original experiment. This work demonstrates the importance of replication of predictive modeling research in MOOCs using large and diverse datasets, illuminates the challenges of doing so, and describes our freely available, open-source software framework to overcome barriers to replication.","PeriodicalId":20634,"journal":{"name":"Proceedings of the Fifth Annual ACM Conference on Learning at Scale","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87380501","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: 25

Electronixtutor integrates multiple learning resources to teach electronics on the web Electronixtutor集成了多种学习资源，在网上教授电子知识

Proceedings of the Fifth Annual ACM Conference on Learning at Scale Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231691

Brent Morgan, Andrew J. Hampton, Zhiqiang Cai, Andrew Tackett, Lijia Wang, Xiangen Hu, A. Graesser

引用次数: 3