Proceedings of the 1st International Conference on Learning Analytics and Knowledge最新文献

Learning analytics to identify exploratory dialogue within synchronous text chat 学习分析以识别同步文本聊天中的探索性对话

Proceedings of the 1st International Conference on Learning Analytics and Knowledge Pub Date : 2011-02-27 DOI: 10.1145/2090116.2090130

Rebecca Ferguson, S. B. Shum

{"title":"Learning analytics to identify exploratory dialogue within synchronous text chat","authors":"Rebecca Ferguson, S. B. Shum","doi":"10.1145/2090116.2090130","DOIUrl":"https://doi.org/10.1145/2090116.2090130","url":null,"abstract":"While generic web analytics tend to focus on easily harvested quantitative data, Learning Analytics will often seek qualitative understanding of the context and meaning of this information. This is critical in the case of dialogue, which may be employed to share knowledge and jointly construct understandings, but which also involves many superficial exchanges. Previous studies have validated a particular pattern of 'exploratory dialogue' in learning environments to signify sharing, challenge, evaluation and careful consideration by participants. This study investigates the use of sociocultural discourse analysis to analyse synchronous text chat during an online conference. Key words and phrases indicative of exploratory dialogue were identified in these exchanges, and peaks of exploratory dialogue were associated with periods set aside for discussion and keynote speakers. Fewer individuals posted at these times, but meaningful discussion outweighed trivial exchanges. If further analysis confirms the validity of these markers as learning analytics, they could be used by recommendation engines to support learners and teachers in locating dialogue exchanges where deeper learning appears to be taking place.","PeriodicalId":150927,"journal":{"name":"Proceedings of the 1st International Conference on Learning Analytics and Knowledge","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123949109","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: 74

Applying analytics for a learning portal: the Organic.Edunet case study 将分析应用于学习门户:有机。Edunet案例研究

Proceedings of the 1st International Conference on Learning Analytics and Knowledge Pub Date : 2011-02-27 DOI: 10.1145/2090116.2090138

N. Palavitsinis, V. Protonotarios, N. Manouselis

{"title":"Applying analytics for a learning portal: the Organic.Edunet case study","authors":"N. Palavitsinis, V. Protonotarios, N. Manouselis","doi":"10.1145/2090116.2090138","DOIUrl":"https://doi.org/10.1145/2090116.2090138","url":null,"abstract":"Learning portals are education-oriented Web portals, which provide access to a variety of educational material, usually coming from various sources. In order to explore how they can support their users during an educational activity (e.g. preparation of teaching a course), it would be interesting to study the behavior of their visitors, focusing on the particular context in which specific actions are taking place. For example, user activities may be analyzed during specific learning events, when activities are more focused. This paper discusses the case study of the Organic.Edunet Web portal (www.organic-edunet.eu), a learning portal for organic agriculture educators that provides access to more than 10,500 learning resources from a federation of 11 institutional repositories. The portal mostly focuses on serving school teachers and university tutors and has attracted until today almost 42.200 unique visitors from more than 160 countries, out of which about 2.600 have registered to the portal. An effort is made to study the users' behavior, focusing in tutors and educators in both schools and universities, in relation to specific training events in which we know that they have been involved. Therefore, we analyze logs of user activities that took place on specific dates and geographical locations, in order to potentially identify notable changes in their normal visiting behavior.","PeriodicalId":150927,"journal":{"name":"Proceedings of the 1st International Conference on Learning Analytics and Knowledge","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121538921","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

A unified framework for multi-level analysis of distributed learning 分布式学习多层次分析的统一框架

Proceedings of the 1st International Conference on Learning Analytics and Knowledge Pub Date : 2011-02-27 DOI: 10.1145/2090116.2090124

D. Suthers, Devan Rosen

{"title":"A unified framework for multi-level analysis of distributed learning","authors":"D. Suthers, Devan Rosen","doi":"10.1145/2090116.2090124","DOIUrl":"https://doi.org/10.1145/2090116.2090124","url":null,"abstract":"Learning and knowledge creation is often distributed across multiple media and sites in networked environments. Traces of such activity may be fragmented across multiple logs and may not match analytic needs. As a result, the coherence of distributed interaction and emergent phenomena are analytically cloaked. Understanding distributed learning and knowledge creation requires multi-level analysis of the situated accomplishments of individuals and small groups and of how this local activity gives rise to larger phenomena in a network. We have developed an abstract transcript representation that provides a unified analytic artifact of distributed activity, and an analytic hierarchy that supports multiple levels of analysis. Log files are abstracted to directed graphs that record observed relationships (contingencies) between events, which may be interpreted as evidence of interaction and other influences between actors. Contingency graphs are further abstracted to two-mode directed graphs that record how associations between actors are mediated by digital artifacts and summarize sequential patterns of interaction. Transitive closure of these associograms creates sociograms, to which existing network analytic techniques may be applied, yielding aggregate results that can then be interpreted by reference to the other levels of analysis. We discuss how the analytic hierarchy bridges between levels of analysis and theory.","PeriodicalId":150927,"journal":{"name":"Proceedings of the 1st International Conference on Learning Analytics and Knowledge","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130866361","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: 71

Learnometrics: metrics for learning objects 学习度量:学习对象的度量

Proceedings of the 1st International Conference on Learning Analytics and Knowledge Pub Date : 2011-02-27 DOI: 10.1145/2090116.2090117

X. Ochoa

{"title":"Learnometrics: metrics for learning objects","authors":"X. Ochoa","doi":"10.1145/2090116.2090117","DOIUrl":"https://doi.org/10.1145/2090116.2090117","url":null,"abstract":"The field of Technology Enhanced Learning (TEL) in general, has the potential to solve one of the most important challenges of our time: enable everyone to learn anything, anytime, anywhere. However, if we look back at more than 50 years of research in TEL, it is not clear where we are in terms of reaching our goal and whether we are, indeed, moving forward. The pace at which technology and new ideas evolve have created a rapid, even exponential, rate of change. This rapid change, together with the natural difficulty to measure the impact of technology in something as complex as learning, has lead to a field with abundance of new, good ideas and scarcity of evaluation studies. This lack of evaluation has resulted into the duplication of efforts and a sense of no \"ground truth\" or \"basic theory' of TEL. This article is an attempt to stop, look back and measure, if not the impact, at least the status of a small fraction of TEL, Learning Object Technologies, in the real world. The measured apparent inexistence of the reuse paradox, the two phase linear growth of repositories or the ineffective metadata quality assessment of humans are clear reminders that even bright theoretical discussions do not compensate the lack of experimentation and measurement. Both theoretical and empirical studies should go hand in hand in order to advance the status of the field. This article is an invitation to other researchers in the field to apply Informetric techniques to measure, understand and apply in their tools the vast amount of information generated by the usage of Technology Enhanced Learning systems.","PeriodicalId":150927,"journal":{"name":"Proceedings of the 1st International Conference on Learning Analytics and Knowledge","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127532422","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: 55

Learning networks, crowds and communities 学习网络、人群和社区

Proceedings of the 1st International Conference on Learning Analytics and Knowledge Pub Date : 2011-02-27 DOI: 10.1145/2090116.2090119

C. Haythornthwaite

引用次数: 30

iSpot analysed: participatory learning and reputation iSpot分析:参与式学习和声誉

Proceedings of the 1st International Conference on Learning Analytics and Knowledge Pub Date : 2011-02-27 DOI: 10.1145/2090116.2090121

D. Clow, E. Makriyannis

引用次数: 74

Generating predictive models of learner community dynamics 生成学习者群体动态的预测模型

Proceedings of the 1st International Conference on Learning Analytics and Knowledge Pub Date : 2011-02-27 DOI: 10.1145/2090116.2090139

Chris Teplovs, Nobuko Fujita, Ravikiran Vatrapu

引用次数: 16

Variable construction for predictive and causal modeling of online education data 在线教育数据预测和因果模型的变量构建

Proceedings of the 1st International Conference on Learning Analytics and Knowledge Pub Date : 2011-02-27 DOI: 10.1145/2090116.2090123

Stephen E. Fancsali

引用次数: 21

Usage contexts for object similarity: exploratory investigations 对象相似性的使用语境:探索性调查

Proceedings of the 1st International Conference on Learning Analytics and Knowledge Pub Date : 2011-02-27 DOI: 10.1145/2090116.2090127

K. Niemann, Hans-Christian Schmitz, Maren Scheffel, M. Wolpers

引用次数: 16

Discourse-centric learning analytics 以话语为中心的学习分析

Proceedings of the 1st International Conference on Learning Analytics and Knowledge Pub Date : 2011-02-27 DOI: 10.1145/2090116.2090120

A. Liddo, S. B. Shum, I. Quinto, Michelle Bachler, L. Cannavacciuolo

引用次数: 136