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

{"title":"Educational data mining meets learning analytics","authors":"R. Baker, S. B. Shum, E. Duval, John C. Stamper, David A. Wiley","doi":"10.1145/2330601.2330613","DOIUrl":"https://doi.org/10.1145/2330601.2330613","url":null,"abstract":"W This panel is proposed as a means of promoting mutual learning and continued dialogue between the Educational Data Mining and Learning Analytics communities. EDM has been developing as a community for longer than the LAK conference, so what if anything makes the LAK community different, and where is the common ground?","PeriodicalId":311750,"journal":{"name":"Proceedings of the 2nd International Conference on Learning Analytics and Knowledge","volume":"61 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":"126506191","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 analytics: envisioning a research discipline and a domain of practice","authors":"George Siemens","doi":"10.1145/2330601.2330605","DOIUrl":"https://doi.org/10.1145/2330601.2330605","url":null,"abstract":"Learning analytics are rapidly being implemented in different educational settings, often without the guidance of a research base. Vendors incorporate analytics practices, models, and algorithms from datamining, business intelligence, and the emerging \"big data\" fields. Researchers, in contrast, have built up a substantial base of techniques for analyzing discourse, social networks, sentiments, predictive models, and in semantic content (i.e., \"intelligent\" curriculum). In spite of the currently limited knowledge exchange and dialogue between researchers, vendors, and practitioners, existing learning analytics implementations indicate significant potential for generating novel insight into learning and vital educational practices. This paper presents an integrated and holistic vision for advancing learning analytics as a research discipline and a domain of practices. Potential areas of collaboration and overlap are presented with the intent of increasing the impact of analytics on teaching, learning, and the education system.","PeriodicalId":311750,"journal":{"name":"Proceedings of the 2nd International Conference on Learning Analytics and Knowledge","volume":"11 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":"132029297","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":"Exploring qualitative analytics for e-mentoring relationships building in an online social learning environment","authors":"Haiming Liu, R. Macintyre, Rebecca Ferguson","doi":"10.1145/2330601.2330646","DOIUrl":"https://doi.org/10.1145/2330601.2330646","url":null,"abstract":"The language of mentoring has become established within the workplace and has gained ground within education. As work based education moves online so we see an increased use of what is termed e-mentoring. In this paper we explore some of the challenges of forming and supporting mentoring relationships virtually, and we explore the solutions afforded by online social learning and Web 2.0. Based on a conceptualization of learning network theory derived from the literature and the qualitative learning analytics, we propose that an e-mentoring relationships is mediated by a connection with or through a person or learning objects. We provide an example to illustrate how this might work.","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":"131227876","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":"Challenges and opportunities for learning analytics when formal teaching meets social spaces","authors":"Nazim Rahman, Jon Dron","doi":"10.1145/2330601.2330619","DOIUrl":"https://doi.org/10.1145/2330601.2330619","url":null,"abstract":"Social networking is revolutionizing the world in ways few imagined just a few years ago. The power of social networking technology can also be leveraged to improve education and enhance the instructor and learner experience. Unlike conventional learning management systems, social software environments such as Athabasca Landing provide a persistent space and are flexible enough to support social and learner-led methods of informal, non-formal, and formal learning. Analytics can be used to effectively track and measure personal progress and help uncover extra-curricular factor affecting learner success such as network formation and growth. The paper reports on an attempt to explore this problem through analysis of student behaviour on the Athabasca Landing site within the context of a course. Its findings, explanation, and potential implications are listed. Effects of social learning on learners, based on the learner's behaviour before, during, and after the course are described and discussed. Finally, features of an open source tool created for this analysis, LASSIE is presented.","PeriodicalId":311750,"journal":{"name":"Proceedings of the 2nd International Conference on Learning Analytics and Knowledge","volume":"134 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":"131053141","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":"What to do with actionable intelligence: E2Coach as an intervention engine","authors":"T. McKay, Kate Miller, J. Tritz","doi":"10.1145/2330601.2330627","DOIUrl":"https://doi.org/10.1145/2330601.2330627","url":null,"abstract":"In this paper, we describe a new, analytics driven approach to supporting students in large introductory physics courses. For this project, we have assembled data for more than 49,000 physics students at the University of Michigan. For each, we combine an extensive portrait of background and preparation with details of progress through the course and final outcome. This information allows us to construct models predicting student performance with a dispersion of half a letter grade. We explore residuals to this model, conducting structured interviews with students who did better (and worse) than expected, identifying strategies which lead to student success (and failure) at all levels of preparation. This work was done in preparation for the launch of E2Coach: a computer tailored educational coaching project which provides a model for an intervention engine, capable of dealing with actionable information for thousands of students.","PeriodicalId":311750,"journal":{"name":"Proceedings of the 2nd International Conference on Learning Analytics and Knowledge","volume":"29 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":"124436931","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":"GLASS: a learning analytics visualization tool","authors":"D. Leony, A. Pardo, Luis de la Fuente Valentín, David Sánchez de Castro, C. D. Kloos","doi":"10.1145/2330601.2330642","DOIUrl":"https://doi.org/10.1145/2330601.2330642","url":null,"abstract":"The use of technology in every day tasks enables the possibility to collect large amounts of observations of events taking place in different environments. Most tools are capable of storing a detailed account of the operations executed by users in certain files commonly known as logs. These files can be further analyzed to infer information that is not directly visible such as the most popular applications, times of the day with highest activity, calories burnt after a running session, etc. Graphic visualizations of this data can be used to support this type of analysis as shown in [1]. Visualization can also be applied in the domain of learning experiences to track and analyse the data obtained from both learners and instructors. There are several tools that have been proposed in specific environments such as, for example, in personal learning environments [5], to foster self-reflection and awareness [2], and to support instructors in web-based distance learning [3]. These visualizations need to take into account aspects such as how to access and protect personal data, filter management, multi-user support and availability. In this paper, the web-based visualization platform GLASS (Gradient's Learning Analytics System) is presented. The architecture of the tool has been conceived to support a large number of modular visualizations derived from a common dataset containing a large number of recorded events. The tool was developed following a bottom-up methodology to provide a set of basic operations required by any visualization. The design goal is to provide a highly versatile, modular platform that simplifies the implementation of new visualizations. The main functionality elements considered in GLASS are database access, module management, visualization parameters, and the web interface. The platform uses datasets stored using the CAM schema (Contextualized Attention Metadata) [6]. This schema allows to capture events occurring during the use of various computer applications which, in our case, are the tools used by students when working in a learning environment. The process to obtain events from learning environments has been described in [4]. GLASS is able to connect to more than one CAM database, thus allowing access to events obtained in different contexts. The tool is extensible through the installation of modules. A module is a structured set of scripts and resources that, given a dataset of events and a set of filters, generates at least one visualization. In order to simplify the development of new modules, the platform provides an API to manage common visualizations settings such as the date range and other typical filters. A visualization may include a simpler version suitable to be displayed in the user's Dashboard, which is the entry page of the application. Figure 1 shows an example of dashboard in GLASS. Additionally, visualizations can be exported as HTML code to be embedded in another website. The GLASS architecture consists of","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":"116268752","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":"Applying artificial intelligence to the educational data: an example of syllabus quality analysis","authors":"Denis Smolin, S. Butakov","doi":"10.1145/2330601.2330644","DOIUrl":"https://doi.org/10.1145/2330601.2330644","url":null,"abstract":"Developing new courses and updating existing ones are routine activities for an educator. The quality of a new or updated course depends on the course structure as well as its individual elements. The syllabus defines the structure and the details of the course, thus contributing to the overall quality of the course. This research proposes a new AI based framework to manage the quality of the syllabus. We apply AI methods to automatically evaluate a syllabus on the basis of such characteristics as validity, usability, and efficiency. We provide user trials to show the advantages of the developed approach against the traditional human-based process of syllabi verification and evaluation.","PeriodicalId":311750,"journal":{"name":"Proceedings of the 2nd International Conference on Learning Analytics and Knowledge","volume":"42 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":"117232241","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":"Seeing what the system thinks you know: visualizing evidence in an open learner model","authors":"Barbara Kump, C. Seifert, G. Beham, Stefanie N. Lindstaedt, Tobias Ley","doi":"10.1145/2330601.2330640","DOIUrl":"https://doi.org/10.1145/2330601.2330640","url":null,"abstract":"User knowledge levels in adaptive learning systems can be assessed based on user interactions that are interpreted as Knowledge Indicating Events (KIE). Such an approach makes complex inferences that may be hard to understand for users, and that are not necessarily accurate. We present MyExperiences, an open learner model designed for showing the users the inferences about them, as well as the underlying data. MyExperiences is one of the first open learner models based on tree maps. It constitutes an example of how research into open learner models and information visualization can be combined in an innovative way.","PeriodicalId":311750,"journal":{"name":"Proceedings of the 2nd International Conference on Learning Analytics and Knowledge","volume":"72 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":"129340320","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":"Cyberlearners and learning resources","authors":"Leyla Zhuhadar, Rong Yang","doi":"10.1145/2330601.2330621","DOIUrl":"https://doi.org/10.1145/2330601.2330621","url":null,"abstract":"The discovery of community structure in real world networks has transformed the way we explore large systems. We propose a visual method to extract communities of cyberlearners in a large interconnected network consisting of cyberlearners and learning resources. The method used is heuristic and is based on visual clustering and a modularity measure. Each cluster of users is considered as a subset of the community of learners sharing a similar domain of interest. Accordingly, a recommender system is proposed to predict and recommend learning resources to cyberlearners within the same community. Experiments on real, dynamic data reveal the structure of community in the network. Our approach used the optimal discovered structure based on the modularity value to design a recommender system.","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":"130513373","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":"Does the length of time off-task matter?","authors":"Danielle M Roberge, Anthony Rojas, R. Baker","doi":"10.1145/2330601.2330657","DOIUrl":"https://doi.org/10.1145/2330601.2330657","url":null,"abstract":"We investigate the relationship between a student's time off-task and the amount that he or she learns to see whether or not the relationship between time off-task and learning is a more complex model than the traditional linear model typically studied. The data collected is based off of students' interactions with Cognitive Tutor learning software. Analysis suggested that more complex functions did not fit the data significantly better than a linear function. In addition, there was not evidence that the length of a specific pause matters for predicting learning outcomes; e.g. students who make many short pauses do not appear to learn more or less than students who make a smaller number of long pauses. As such, previous theoretical accounts arguing that off-task behavior primarily reduces learning by reducing the amount of time spent learning remain congruent with the current evidence.","PeriodicalId":311750,"journal":{"name":"Proceedings of the 2nd International Conference on Learning Analytics and Knowledge","volume":"32 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":"116551546","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}