Proceedings of the Fifth Annual ACM Conference on Learning at Scale最新文献_第5页

The unbearable lightness of consent: mapping MOOC providers' response to consent 无法忍受的同意之轻:绘制MOOC提供者对同意的反应

Proceedings of the Fifth Annual ACM Conference on Learning at Scale Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231659

Mohammad Khalil, P. Prinsloo, Sharon Slade

{"title":"The unbearable lightness of consent: mapping MOOC providers' response to consent","authors":"Mohammad Khalil, P. Prinsloo, Sharon Slade","doi":"10.1145/3231644.3231659","DOIUrl":"https://doi.org/10.1145/3231644.3231659","url":null,"abstract":"While many strategies for protecting personal privacy have relied on regulatory frameworks, consent and anonymizing data, such approaches are not always effective. Frameworks and Terms and Conditions often lag user behaviour and advances in technology and software; consent can be provisional and fragile; and the anonymization of data may impede personalized learning. This paper reports on a dialogical multi-case study methodology of four Massive Open Online Course (MOOC) providers from different geopolitical and regulatory contexts. It explores how the providers (1) define 'personal data' and whether they acknowledge a category of 'special' or 'sensitive' data; (2) address the issue and scope of student consent (and define that scope); and (3) use student data in order to inform pedagogy and/or adapt the learning experience to personalise the context or to increase student retention and success rates. This study found that large amounts of personal data continue to be collected for purposes seemingly unrelated to the delivery and support of courses. The capacity for users to withdraw or withhold consent for the collection of certain categories of data such as sensitive personal data remains severely constrained. This paper proposes that user consent at the time of registration should be reconsidered, and that there is a particular need for consent when sensitive personal data are used to personalize learning, or for purposes outside the original intention of obtaining consent.","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":"86971887","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: 10

The impact of the peer review process evolution on learner performance in e-learning environments 网络学习环境下同伴评议过程演变对学习者绩效的影响

Proceedings of the Fifth Annual ACM Conference on Learning at Scale Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231693

M. Montebello, Petrilson Pinheiro, B. Cope, M. Kalantzis, Tabassum Amina, Duane Searsmith, D. Cao

引用次数: 6

Managing and analyzing student learning data: a python-based solution for edX 管理和分析学生学习数据:一个基于python的edX解决方案

Proceedings of the Fifth Annual ACM Conference on Learning at Scale Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231706

Vita Lampietti, Anindya Roy, Sheryl Barnes

引用次数: 0

Use expert knowledge instead of data: generating hints for hour of code exercises 使用专业知识而不是数据:为几个小时的代码练习生成提示

Proceedings of the Fifth Annual ACM Conference on Learning at Scale Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231690

M. Buwalda, J. Jeuring, N. Naus

引用次数: 2

Measuring item similarity in introductory programming 在入门编程中测量项目的相似性

Proceedings of the Fifth Annual ACM Conference on Learning at Scale Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231676

Radek Pelánek, Tomáš Effenberger, Matej Vanek, Vojtech Sassmann, Dominik Gmiterko

引用次数: 7

XIPIt

Proceedings of the Fifth Annual ACM Conference on Learning at Scale Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231696

Duygu Bektik

引用次数: 0

Towards adapting to learners at scale: integrating MOOC and intelligent tutoring frameworks 面向大规模适应学习者:整合MOOC和智能辅导框架

Proceedings of the Fifth Annual ACM Conference on Learning at Scale Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231671

V. Aleven, J. Sewall, J. M. Andres, R. Sottilare, Rodney A. Long, R. Baker

引用次数: 17

How do professors format exams?: an analysis of question variety at scale 教授是如何安排考试的?对问题多样性的分析

Proceedings of the Fifth Annual ACM Conference on Learning at Scale Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231667

Paul Laskowski, Sergey Karayev, Marti A. Hearst

{"title":"How do professors format exams?: an analysis of question variety at scale","authors":"Paul Laskowski, Sergey Karayev, Marti A. Hearst","doi":"10.1145/3231644.3231667","DOIUrl":"https://doi.org/10.1145/3231644.3231667","url":null,"abstract":"This study analyzes the use of paper exams in college-level STEM courses. It leverages a unique dataset of nearly 1,800 exams, which were scanned into a web application, then processed by a team of annotators to yield a detailed snapshot of the way instructors currently structure exams. The focus of the investigation is on the variety of question formats, and how they are applied across different course topics. The analysis divides questions according to seven top-level categories, finding significant differences among these in terms of positioning, use across subjects, and student performance. The analysis also reveals a strong tendency within the collection for instructors to order questions from easier to harder. A linear mixed effects model is used to estimate the reliability of different question types. Long writing questions stand out for their high reliability, while binary and multiple choice questions have low reliability. The model suggests that over three multiple choice questions, or over five binary questions, are required to attain the same reliability as a single long writing question. A correlation analysis across seven response types finds that student abilities for different questions types exceed 70 percent for all pairs, although binary and multiple-choice questions stand out for having unusually low correlations with all other question types.","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":"76245218","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

How a data-driven course planning tool affects college students' GPA: evidence from two field experiments 数据驱动的课程规划工具如何影响大学生的GPA:来自两个实地实验的证据

Proceedings of the Fifth Annual ACM Conference on Learning at Scale Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231668

Sorathan Chaturapruek, T. Dee, Ramesh Johari, René F. Kizilcec, M. Stevens

{"title":"How a data-driven course planning tool affects college students' GPA: evidence from two field experiments","authors":"Sorathan Chaturapruek, T. Dee, Ramesh Johari, René F. Kizilcec, M. Stevens","doi":"10.1145/3231644.3231668","DOIUrl":"https://doi.org/10.1145/3231644.3231668","url":null,"abstract":"College students rely on increasingly data-rich environments when making learning-relevant decisions about the courses they take and their expected time commitments. However, we know little about how their exposure to such data may influence student course choice, effort regulation, and performance. We conducted a large-scale field experiment in which all the undergraduates at a large, selective university were randomized to an encouragement to use a course-planning web application that integrates information from official transcripts from the past fifteen years with detailed end-of-course evaluation surveys. We found that use of the platform lowered students' GPA by 0.28 standard deviations on average. In a subsequent field experiment, we varied access to information about course grades and time commitment on the platform and found that access to grade information in particular lowered students' overall GPA. Our exploratory analysis suggests these effects are not due to changes in the portfolio of courses that students choose, but rather by changes to their behavior within courses.","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":"72801200","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: 26

Toward large-scale learning design: categorizing course designs in service of supporting learning outcomes 面向大规模学习设计:分类课程设计以支持学习成果

Proceedings of the Fifth Annual ACM Conference on Learning at Scale Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231663

Dan Davis, Daniel T. Seaton, C. Hauff, G. Houben

引用次数: 15