Proceedings of the Fifth Annual ACM Conference on Learning at Scale最新文献

Towards making block-based programming activities adaptive 使基于块的编程活动具有适应性

Proceedings of the Fifth Annual ACM Conference on Learning at Scale Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231670

Tomáš Effenberger, Radek Pelánek

引用次数: 21

Toward a large-scale open learning system for data management 面向数据管理的大规模开放式学习系统

Proceedings of the Fifth Annual ACM Conference on Learning at Scale Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231673

S. Murthy, Andrew Figueroa, Steven Rollo

引用次数: 1

Exploring the utility of response times and wrong answers for adaptive learning 探索响应时间和错误答案在适应性学习中的效用

Proceedings of the Fifth Annual ACM Conference on Learning at Scale Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231675

Radek Pelánek

引用次数: 9

Virtualizing face-2-face trainings for training senior professionals: a comparative case study on financial auditors 面向高级专业人员培训的虚拟化面对面培训:财务审计师的比较案例研究

Proceedings of the Fifth Annual ACM Conference on Learning at Scale Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231695

Viktoria Pammer-Schindler, S. Thalmann, Angela Fessl, Julia Füssel

引用次数: 1

Grading at scale in earsketch 在草图中按比例分级

Proceedings of the Fifth Annual ACM Conference on Learning at Scale Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231708

Avneesh Sarwate, Creston Brunch, Jason Freeman, S. Siva

引用次数: 5

Towards domain general detection of transactive knowledge building behavior 面向交互知识构建行为的领域通用检测

Proceedings of the Fifth Annual ACM Conference on Learning at Scale Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231655

James Fiacco, C. Rosé

{"title":"Towards domain general detection of transactive knowledge building behavior","authors":"James Fiacco, C. Rosé","doi":"10.1145/3231644.3231655","DOIUrl":"https://doi.org/10.1145/3231644.3231655","url":null,"abstract":"Support of discussion based learning at scale benefits from automated analysis of discussion for enabling effective assignment of students to project teams, for triggering dynamic support of group learning processes, and for assessment of those learning processes. A major limitation of much past work in machine learning applied to automated analysis of discussion is the failure of the models to generalize to data outside of the parameters of the context in which the training data was collected. This limitation means that a separate training effort must be undertaken for each domain in which the models will be used. This paper focuses on a specific construct of discussion based learning referred to as Transactivity and provides a novel machine learning approach with performance that exceeds state-of-the-art performance within the same domain in which it was trained and a new domain, and does not suffer any reduction in performance when transferring to the new domain. These results stand as an advance over past work on automated detection of Transactivity and increase the value of trained models for supporting group learning at scale. Implications for practice in at-scale learning environments are discussed.","PeriodicalId":20634,"journal":{"name":"Proceedings of the Fifth Annual ACM Conference on Learning at Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81473309","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: 3

Gamifying higher education: enhancing learning with mobile game app 高等教育游戏化:利用手机游戏app加强学习

Proceedings of the Fifth Annual ACM Conference on Learning at Scale Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231686

Farshida Zafar, Jacqueline Wong, Mohammad Khalil

引用次数: 6

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":null,"pages":null},"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

Effects of automated interventions in programming assignments: evidence from a field experiment 编程作业中自动化干预的影响:来自现场实验的证据

Proceedings of the Fifth Annual ACM Conference on Learning at Scale Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231650

Ralf Teusner, Thomas Hille, T. Staubitz

{"title":"Effects of automated interventions in programming assignments: evidence from a field experiment","authors":"Ralf Teusner, Thomas Hille, T. Staubitz","doi":"10.1145/3231644.3231650","DOIUrl":"https://doi.org/10.1145/3231644.3231650","url":null,"abstract":"A typical problem in MOOCs is the missing opportunity for course conductors to individually support students in overcoming their problems and misconceptions. This paper presents the results of automatically intervening on struggling students during programming exercises and offering peer feedback and tailored bonus exercises. To improve learning success, we do not want to abolish instructionally desired trial and error but reduce extensive struggle and demotivation. Therefore, we developed adaptive automatic just-in-time interventions to encourage students to ask for help if they require considerably more than average working time to solve an exercise. Additionally, we offered students bonus exercises tailored for their individual weaknesses. The approach was evaluated within a live course with over 5,000 active students via a survey and metrics gathered alongside. Results show that we can increase the call outs for help by up to 66% and lower the dwelling time until issuing action. Learnings from the experiments can further be used to pinpoint course material to be improved and tailor content to be audience specific.","PeriodicalId":20634,"journal":{"name":"Proceedings of the Fifth Annual ACM Conference on Learning at Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80557055","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: 14