Proceedings of the 2nd International Conference on Learning Analytics and Knowledge最新文献

{"title":"Goal-oriented visualizations of activity tracking: a case study with engineering students","authors":"José Luís Santos, Sten Govaerts, K. Verbert, E. Duval","doi":"10.1145/2330601.2330639","DOIUrl":"https://doi.org/10.1145/2330601.2330639","url":null,"abstract":"Increasing motivation of students and helping them to reflect on their learning processes is an important driver for learning analytics research. This paper presents our research on the development of a dashboard that enables self-reflection on activities and comparison with peers. We describe evaluation results of four iterations of a design based research methodology that assess the usability, use and usefulness of different visualizations. Lessons learned from the different evaluations performed during each iteration are described. In addition, these evaluations illustrate that the dashboard is a useful tool for students. However, further research is needed to assess the impact on the learning process.","PeriodicalId":311750,"journal":{"name":"Proceedings of the 2nd International Conference on Learning Analytics and Knowledge","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127505390","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}

{"title":"Visual analytics in support of education","authors":"K. Börner","doi":"10.1145/2330601.2330604","DOIUrl":"https://doi.org/10.1145/2330601.2330604","url":null,"abstract":"The amount of data about us and our world is increasing rapidly, and the capability to analyze large data sets---so-called big data---becomes a key basis of competition, underpinning new waves of productivity growth and innovation. The big data phenomenon is fueled by cheap sensors and high-throughput simulation models, the increasing volume and detail of information captured by enterprises, the rise of multimedia, social media, and the Internet. It exists from social media to cell biology offering unparalleled opportunities to document the inner workings of many complex systems [1]. Research by MGI and McKinsey's Business Technology Office argues that there will be a shortage of talent necessary for organizations to take advantage of big data. \"By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions\" [2]. In everyday life, people deal with large amounts of data regularly: online search engines provide access to millions of web sites almost instantly; consumer sites offer literally thousands of purchase options seamlessly; and social media sites let you create and benefit from extensive social networks. In bestselling books like Freakonomics, Super Crunchers and The Numerati, authors illuminate how more and more decisions in health care, politics, education, and other sectors utilize big data and data analysis [3]. The texts highlight the growing need for specialists and every-day citizens to be able to understand and interpret data. Whether it is a table of nutritional information, a graph of stock prices, or a chart comparing health care plans, the skills of understanding and interpreting data are necessary to navigate successfully through daily life. This talk starts with a review of visual analytics projects that aim to increase our understanding of how people learn, increase the efficacy of learning environments, or support decision making in education [4]. The second part of the talk provides a theoretical framework for the design of effective data analysis workflows and insightful visualizations. It also introduces plug-and-play macroscope tools [5], see also http://cishell.org, that were designed for different research communities and are used by more than 120,000 users from 40+ countries to design and benefit from visualizations of complex data. The talk concludes with a discussion of challenges that arise when visual analytics tools are introduced to classrooms and informal science education.","PeriodicalId":311750,"journal":{"name":"Proceedings of the 2nd International Conference on Learning Analytics and Knowledge","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123316761","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}

{"title":"Learning dispositions and transferable competencies: pedagogy, modelling and learning analytics","authors":"S. B. Shum, R. Crick","doi":"10.1145/2330601.2330629","DOIUrl":"https://doi.org/10.1145/2330601.2330629","url":null,"abstract":"Theoretical and empirical evidence in the learning sciences substantiates the view that deep engagement in learning is a function of a complex combination of learners' identities, dispositions, values, attitudes and skills. When these are fragile, learners struggle to achieve their potential in conventional assessments, and critically, are not prepared for the novelty and complexity of the challenges they will meet in the workplace, and the many other spheres of life which require personal qualities such as resilience, critical thinking and collaboration skills. To date, the learning analytics research and development communities have not addressed how these complex concepts can be modelled and analysed, and how more traditional social science data analysis can support and be enhanced by learning analytics. We report progress in the design and implementation of learning analytics based on a research validated multidimensional construct termed \"learning power\". We describe, for the first time, a learning analytics infrastructure for gathering data at scale, managing stakeholder permissions, the range of analytics that it supports from real time summaries to exploratory research, and a particular visual analytic which has been shown to have demonstrable impact on learners. We conclude by summarising the ongoing research and development programme and identifying the challenges of integrating traditional social science research, with learning analytics and modelling.","PeriodicalId":311750,"journal":{"name":"Proceedings of the 2nd International Conference on Learning Analytics and Knowledge","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129986954","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}

{"title":"Modelling learning & performance: a social networks perspective","authors":"Walter Christian Paredes, K. S. Chung","doi":"10.1145/2330601.2330617","DOIUrl":"https://doi.org/10.1145/2330601.2330617","url":null,"abstract":"Traditional models of learning using a sociological perspective include social learning, situated learning and models of connectivisim and self-efficacy. While these models explain how individuals learn in varying social dimensions, very few studies provide empirical validation of such models and extend them to include group learning and performance. In this exploratory study, we develop a theoretical model based on social learning and social network theories to understand how knowledge professionals engage in learning and performance, both as individuals and as groups. We investigate the association between egocentric network properties (structure, position and tie), 'content richness' in the social learning process and performance. Analysis from data collected using an online eLearning environment shows that rather than performance; social learning is influenced by properties of social network structure (density, inter-group and intra-network communication), relations (tie strength) and position (efficiency). Furthermore, individuals who communicate with others internal rather than external to the group show higher tendencies of social learning. The contribution of this study is therefore two-fold: a theoretical development of a social learning and networks based model for understanding learning and performance; and the construction of a novel metric called 'content richness' as a surrogate measure for social learning. In conclusion, a useful implication of the study is that the model fosters understanding social factors that influence learning and performance in the domain of learning analytics. It also begs the question of whether the relationship between social networks and performance is mediated or moderated by learning and whether assumptions of the model hold true in non-educational domains.","PeriodicalId":311750,"journal":{"name":"Proceedings of the 2nd International Conference on Learning Analytics and Knowledge","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131877049","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}

{"title":"Clustering by usage: higher order co-occurrences of learning objects","authors":"K. Niemann, Hans-Christian Schmitz, Uwe Kirschenmann, M. Wolpers, Anna Schmidt, Tim Krones","doi":"10.1145/2330601.2330659","DOIUrl":"https://doi.org/10.1145/2330601.2330659","url":null,"abstract":"In this paper, we introduce a new way of detecting semantic similarities between learning objects by analyzing their usage in a web portal. Our approach does not rely on the content of the learning objects or on the relations between the users and the learning objects but on usage-based relations between the objects themselves. The technique we apply for calculating higher order co-occurrences to create semantically homogenous clusters of data objects is taken from corpus driven lexicology where it is used to cluster words. We expect the members of a higher order co-occurrence class to be similar according to their content and present the evaluations of that assumption using two teaching and learning systems.","PeriodicalId":311750,"journal":{"name":"Proceedings of the 2nd International Conference on Learning Analytics and Knowledge","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132572136","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}

{"title":"Building organizational capacity for analytics: panel proposal","authors":"Donald M. Norris, Linda Baer","doi":"10.1145/2330601.2330612","DOIUrl":"https://doi.org/10.1145/2330601.2330612","url":null,"abstract":"The field of analytics is mushrooming. Analytics is perceived as the potential decoder for institutional transformation. Given the mandates for improved assessment, accountability and performance, analytical tools are in high demand. A critical goal is to optimize student success by managing the student pipeline to success, eliminating structural and programmatic impediments to retention and success and by utilizing dynamic query, reporting, intervention and embedded predictive analytics to respond to at-risk behavior. Additional optimization practices are emerging as well: Expanded data mining, early-stage learner relationship management practices, and consideration of employability success. This panel presentation will describe the tools leading-edge institutions are using and what tools vendors are offering. The gap between supply and demand will be the main focus of the session.","PeriodicalId":311750,"journal":{"name":"Proceedings of the 2nd International Conference on Learning Analytics and Knowledge","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116853223","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}

{"title":"Building a data governance model for learning analytics","authors":"S. Graf, C. Ives, Lori Lockyer, P. Hobson, D. Clow","doi":"10.1145/2330601.2330614","DOIUrl":"https://doi.org/10.1145/2330601.2330614","url":null,"abstract":"This international panel presentation aims to explore and discuss the issues that emerge when an educational institution decides to develop learning analytics initiatives. While learning analytics may provide data that lead to improvements in the quality of teaching and learning design, and therefore has the potential to enhance the overall quality of education, the successful development and implementation of tools and processes for learning analytics are complex and problematic. In this panel, data governance considerations will be discussed from organizational, ethical, learning design, and technical points of view.","PeriodicalId":311750,"journal":{"name":"Proceedings of the 2nd International Conference on Learning Analytics and Knowledge","volume":"124 23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115833063","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}

{"title":"The learning analytics cycle: closing the loop effectively","authors":"D. Clow","doi":"10.1145/2330601.2330636","DOIUrl":"https://doi.org/10.1145/2330601.2330636","url":null,"abstract":"This paper develops Campbell and Oblinger's [4] five-step model of learning analytics (Capture, Report, Predict, Act, Refine) and other theorisations of the field, and draws on broader educational theory (including Kolb and Schön) to articulate an incrementally more developed, explicit and theoretically-grounded Learning Analytics Cycle. This cycle conceptualises successful learning analytics work as four linked steps: learners (1) generating data (2) that is used to produce metrics, analytics or visualisations (3). The key step is 'closing the loop' by feeding back this product to learners through one or more interventions (4). This paper seeks to begin to place learning analytics practice on a base of established learning theory, and draws several implications from this theory for the improvement of learning analytics projects. These include speeding up or shortening the cycle so feedback happens more quickly, and widening the audience for feedback (in particular, considering learners and teachers as audiences for analytics) so that it can have a larger impact.","PeriodicalId":311750,"journal":{"name":"Proceedings of the 2nd International Conference on Learning Analytics and Knowledge","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126907521","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}

{"title":"Course correction: using analytics to predict course success","authors":"Rebecca T Barber, Mike Sharkey","doi":"10.1145/2330601.2330664","DOIUrl":"https://doi.org/10.1145/2330601.2330664","url":null,"abstract":"Predictive analytics techniques applied to a broad swath of student data can aid in timely intervention strategies to help prevent students from failing a course. This paper discusses a predictive analytic model that was created for the University of Phoenix. The purpose of the model is to identify students who are in danger of failing the course in which they are currently enrolled. Within the model's architecture, data from the learning management system (LMS), financial aid system, and student system are combined to calculate a likelihood of any given student failing the current course. The output can be used to prioritize students for intervention and referral to additional resources. The paper includes a discussion of the predictor and statistical tests used, validation procedures, and plans for implementation.","PeriodicalId":311750,"journal":{"name":"Proceedings of the 2nd International Conference on Learning Analytics and Knowledge","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125194952","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}

{"title":"Educational monitoring tool based on faceted browsing and data portraits","authors":"David García-Solórzano, J. A. Morán, Germán Cobo, Carlos Monzo, Eugènia Santamaria, J. Melenchón","doi":"10.1145/2330601.2330645","DOIUrl":"https://doi.org/10.1145/2330601.2330645","url":null,"abstract":"Due to the idiosyncrasy of online education, students may become disoriented, frustrated or confused if they do not receive the support, feedback or guidance needed to be successful. To avoid this, the role of teachers is essential. In this regard, instructors should be facilitators who guide students throughout the teaching-learning process and arrange meaningful learner-centered experiences. However, unlike face-to-face classes, teachers have difficulty in monitoring their learners in an online environment, since a lot of learning management systems provide faculty with student tracking data in a poor tabular format that is difficult to understand. In order to overcome this drawback, this paper presents a novel graphical educational monitoring tool based on faceted browsing that helps instructors to gain an insight into their classrooms' performance. Moreover, this tool depicts information of each individual student by using a data portrait. Thanks to this monitoring tool, teachers can, on the one hand, track their students during the teaching-learning process and, on the other, detect potential problems in time.","PeriodicalId":311750,"journal":{"name":"Proceedings of the 2nd International Conference on Learning Analytics and Knowledge","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114671498","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}