International Conference on Learning Analytics and Knowledge最新文献_第10页

Crafting transformative strategies for personalized learning/analytics 为个性化学习/分析制定变革策略

International Conference on Learning Analytics and Knowledge Pub Date : 2013-04-08 DOI: 10.1145/2460296.2460354

L. Baer, A. Duin, D. Norris, Robert Brodnick

{"title":"Crafting transformative strategies for personalized learning/analytics","authors":"L. Baer, A. Duin, D. Norris, Robert Brodnick","doi":"10.1145/2460296.2460354","DOIUrl":"https://doi.org/10.1145/2460296.2460354","url":null,"abstract":"Personalized learning environments and learning analytics hold the promise to transform learning experiences, enhance and accelerate student success, and \"open up\" student learning to resources and experiences from outside individual institutions. To achieve their potential, personalized learning projects must move beyond individual, stand-alone projects or innovations to reshaping the institutional experience.\u0000 Learning science must connect with learning pedagogy and design. Learners and institutions must have access to tools and resources that assist in customizing student progress and supplemental learning needs. Teachers and faculty must be empowered to provide teaching and learning environments that allow individual students to thrive. All this will require unique partnerships and collaborations within and across institutions, incorporating the best learning science findings and bridging with public and private entities developing the learning and analytic tools to support personalized learning.\u0000 Crafting a strategy to embrace and sustain the transformative power of personalized learning systems will require strong leadership and clear planning models to align with institutional planning and future investments.","PeriodicalId":162301,"journal":{"name":"International Conference on Learning Analytics and Knowledge","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133556839","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: 7

Affective states and state tests: investigating how affect throughout the school year predicts end of year learning outcomes 情感状态和状态测试:调查整个学年的影响如何预测学年结束时的学习成果

International Conference on Learning Analytics and Knowledge Pub Date : 2013-04-08 DOI: 10.1145/2460296.2460320

Z. Pardos, R. Baker, M. O. S. Pedro, S. M. Gowda, Supreeth M. Gowda

{"title":"Affective states and state tests: investigating how affect throughout the school year predicts end of year learning outcomes","authors":"Z. Pardos, R. Baker, M. O. S. Pedro, S. M. Gowda, Supreeth M. Gowda","doi":"10.1145/2460296.2460320","DOIUrl":"https://doi.org/10.1145/2460296.2460320","url":null,"abstract":"In this paper, we investigate the correspondence between student affect in a web-based tutoring platform throughout the school year and learning outcomes at the end of the year, on a high-stakes mathematics exam. The relationships between affect and learning outcomes have been previously studied, but not in a manner that is both longitudinal and finer-grained. Affect detectors are used to estimate student affective states based on post-hoc analysis of tutor log-data. For every student action in the tutor the detectors give us an estimated probability that the student is in a state of boredom, engaged concentration, confusion, and frustration, and estimates of the probability that they are exhibiting off-task or gaming behaviors. We ran the detectors on two years of log-data from 8th grade student use of the ASSISTments math tutoring system and collected corresponding end of year, high stakes, state math test scores for the 1,393 students in our cohort. By correlating these data sources, we find that boredom during problem solving is negatively correlated with performance, as expected; however, boredom is positively correlated with performance when exhibited during scaffolded tutoring. A similar pattern is unexpectedly seen for confusion. Engaged concentration and frustration are both associated with positive learning outcomes, surprisingly in the case of frustration.","PeriodicalId":162301,"journal":{"name":"International Conference on Learning Analytics and Knowledge","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124795969","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: 151

Improving retention: predicting at-risk students by analysing clicking behaviour in a virtual learning environment 提高记忆:通过分析虚拟学习环境中的点击行为来预测有风险的学生

International Conference on Learning Analytics and Knowledge Pub Date : 2013-04-08 DOI: 10.1145/2460296.2460324

A. Wolff, Z. Zdráhal, A. Nikolov, Michal Pantucek

{"title":"Improving retention: predicting at-risk students by analysing clicking behaviour in a virtual learning environment","authors":"A. Wolff, Z. Zdráhal, A. Nikolov, Michal Pantucek","doi":"10.1145/2460296.2460324","DOIUrl":"https://doi.org/10.1145/2460296.2460324","url":null,"abstract":"One of the key interests for learning analytics is how it can be used to improve retention. This paper focuses on work conducted at the Open University (OU) into predicting students who are at risk of failing their module. The Open University is one of the worlds largest distance learning institutions. Since tutors do not interact face to face with students, it can be difficult for tutors to identify and respond to students who are struggling in time to try to resolve the difficulty. Predictive models have been developed and tested using historic Virtual Learning Environment (VLE) activity data combined with other data sources, for three OU modules. This has revealed that it is possible to predict student failure by looking for changes in user's activity in the VLE, when compared against their own previous behaviour, or that of students who can be categorised as having similar learning behaviour. More focused analysis of these modules applying the GUHA (General Unary Hypothesis Automaton) method of data analysis has also yielded some early promising results for creating accurate hypothesis about students who fail.","PeriodicalId":162301,"journal":{"name":"International Conference on Learning Analytics and Knowledge","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116820746","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: 214

Considering formal assessment in learning analytics within a PLE: the HOU2LEARN case 考虑PLE学习分析中的正式评估:HOU2LEARN案例

International Conference on Learning Analytics and Knowledge Pub Date : 2013-04-08 DOI: 10.1145/2460296.2460304

Eleni Koulocheri, M. Xenos

引用次数: 20

Model-driven assessment of learners in open-ended learning environments 开放式学习环境中学习者的模型驱动评估

International Conference on Learning Analytics and Knowledge Pub Date : 2013-04-08 DOI: 10.1145/2460296.2460336

James Segedy, Kirk M. Loretz, Gautam Biswas

引用次数: 12

An eye-tracking study of notational, informational, and emotional aspects of learning analytics representations 学习分析表征的符号、信息和情感方面的眼动追踪研究

International Conference on Learning Analytics and Knowledge Pub Date : 2013-04-08 DOI: 10.1145/2460296.2460321

Ravikiran Vatrapu, P. Reimann, S. Bull, Matthew D. Johnson

引用次数: 20

Live interest meter: learning from quantified feedback in mass lectures 现场兴趣计:从大量讲座的量化反馈中学习

International Conference on Learning Analytics and Knowledge Pub Date : 2013-04-08 DOI: 10.1145/2460296.2460302

Verónica Rivera-Pelayo, Johannes Munk, V. Zacharias, Simone Braun

引用次数: 47

Issues, challenges, and lessons learned when scaling up a learning analytics intervention 扩大学习分析干预时的问题、挑战和经验教训

International Conference on Learning Analytics and Knowledge Pub Date : 2013-04-08 DOI: 10.1145/2460296.2460343

Steven Lonn, Stephen J. Aguilar, Stephanie D. Teasley

引用次数: 39

Multidisciplinarity vs. Multivocality, the case of "learning analytics" 多学科vs.多声音，“学习分析”案例

International Conference on Learning Analytics and Knowledge Pub Date : 2013-04-08 DOI: 10.1145/2460296.2460299

N. Balacheff, Kristine Lund

引用次数: 14

Learning object analytics for collections, repositories & federations 学习集合、存储库和联合的对象分析

International Conference on Learning Analytics and Knowledge Pub Date : 2013-04-08 DOI: 10.1145/2460296.2460359

M. Sicilia, X. Ochoa, Giannis Stoitsis, J. Klerkx

{"title":"Learning object analytics for collections, repositories & federations","authors":"M. Sicilia, X. Ochoa, Giannis Stoitsis, J. Klerkx","doi":"10.1145/2460296.2460359","DOIUrl":"https://doi.org/10.1145/2460296.2460359","url":null,"abstract":"A large number of curated digital collections containing learning resources of a various kind has emerged in the last year. These include referatories containing descriptions for resources in the Web (as MERLOT), aggregated collections (as Organic.Edunet), concrete initiatives as Khan Academy, repositories hosting and versioning modular content (as Connexions) and meta-aggregators (as Globe and Learning Registry). Also, OpenCourseware and other OER initiatives have contributed to making this ecosystem of resources richer. Very interesting insights can be extracted when studying the usage and social data that are produced within the learning collections, repositories and federations. At the same time, concerns for the quality and sustainability of these collections have been raised, which has lead to research on quality measurement and metrics. The Workshop attempts to bring studies and demonstrations for any kind of analysis done on learning resource collections, from an interdisciplinary perspective. We consider digital collections not as merely IT deployments but as social systems with contributors, owners, evaluators and users forming patterns of interactions on top of portals or through search systems embedded in other learning technology components. This is in coherence of considering these social systems under a Web Science approach (http://webscience.org/).","PeriodicalId":162301,"journal":{"name":"International Conference on Learning Analytics and Knowledge","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114439627","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: 8