Learning Analytics - A New Discipline and Linked Data
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Learning Analytics - A New Discipline and Linked Data

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Learning Analytics - A New Discipline and Linked Data Learning Analytics - A New Discipline and Linked Data Presentation Transcript

  • https://semtech.athabascau.ca Learning Analytics- A New Discipline and Linked Data - http:// Dragan Gašević @dgasevic
  • Today’s idea of “learning”
  • Topics to discuss Learning – today, tomorrow… Learning analytics – definition Learning analytics – approaches & challenges with bits of semantics
  • Part ILearning Today, Tomorrow
  • Learning Pathshttp://www.w3.org/2006/Talks/1023-sb-W3CTechSemWeb/DataServicesWebAppMetro2.jpg
  • Educational Ecosystems Reusability Adaptivity Evolution Collaboration with educators and studentsAuthoring PackagingReusability Educators
  • Educational Ecosystems Feedback Personalization Adaptivity Context-awareness Social interaction …Authoring Packaging Learning and CollaboratingReusability Educators Learners
  • Educational Ecosystems Administration Mobile CommunityAuthoring Packaging Learning and Collaborating Peer-ReviewReusability Presenting Educators Learners
  • Learning (Life) not in a box! A small part of a software engineer’s life
  • Learning ecosystem …… … … … A small part of a software engineer’s life
  • Three Generation of Distance Education PedagogiesAnderson, T. & Dron, J. (2011) Three Generations of Distance Education Pedagogy, InternationalReview of Research in Open and Distance Learning 12(3), 80-97, http://goo.gl/j3mRF
  • Part IILearning Analytics
  • Learning Analytics – What? Measurement, collection, analysis, and reporting of dataabout learners and their contexts
  • Learning Analytics – Why? Understanding and optimising learning and the environments in which learning occurs
  • http://solaresearch.org/OpenLearningAnalytics.pdf
  • Evidence-based EducationAs the integration of best research evidence with practitioner expertise and stakeholder values The goal made up based on
  • Part III Learning AnalyticsApproaches and Challenges - with bits of semantics -
  • Learning Analytics What and how to collect?Measurement, collection, analysis, and reporting of data about learners and their contexts
  • Ubiquitous learning analytics Tool and format independent Aggregates and integrates
  • Semantic Web Ontologies: Interconnecting applications  Shared domain conceptualizations
  • Linked Data http://richard.cyganiak.de/2007/10/lod/
  • Linked Data http://richard.cyganiak.de/2007/10/lod/
  • “A crazy problem requires a crazy solution!” (Griff Richards, 2005)
  • Learning Context Ontology: LOCOJovanovic, J., Knight, C., Gasevic, D., Richards, G., "Ontologies for Effective Use of Context in e-Learning Settings," Educational Technology &Society, Vol. 10, No. 3, 2007, pp. 47-59
  • LOCO-Analyst  OAST and LOCO-AnalystAli, L., Hatala, M. Gašević, D., Jovanović, J., "A Qualitative Evaluation of Evolution of a Learning AnalyticsTool," Computers & Education, Vol. 58, No. 1, 2012, pp. 470-489, http://goo.gl/lCvMT
  • LOCO-Analyst  OAST and LOCO-AnalystAli, L., Hatala, M. Gašević, D., Jovanović, J., "A Qualitative Evaluation of Evolution of a Learning AnalyticsTool," Computers & Education, Vol. 58, No. 1, 2012, pp. 470-489, http://goo.gl/lCvMT
  • LOCO-Analyst
  • Formative Evaluation Category Sub-category Q8 Visualization/GUI 77.8% Suggestions for improving Annotations 5.66% Other Features 11.11% Data Visualization - No suggestions but liked Interface Design 5.56% Annotations -Ali, L., Hatala, M. Gašević, D., Jovanović, J. (2012). A Qualitative Evaluation of Evolution ofa Learning Analytics Tool. Computers & Education, 58(1) 470-489, http://goo.gl/lCvMT
  • Learning Analytics What and how to report?Measurement, collection, analysis, and reporting of data about learners and their contexts
  • Visual learning analyticsUbiquitous LA impossible w/o visual Information overload
  • LOCO-Analyst
  • LOCO-Analyst
  • LOCO-Analyst
  • LOCO-Analyst
  • Student comprehension
  • Student comprehension
  • Learning Analytics Acceptance ModelInspired by Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D., 2003. User acceptance ofinformation technology: Toward a unified view. MIS Quarterly, (27:3), pp. 425-478.
  • Learning Analytics Acceptance ModelAli, L., Asadi, M., Jovanović, J., Gašević, D., Hatala, M., “Factors influencing Perceived Utility and Adoption ofa Learning Analytics Tool: An Empirical Study,” Computers & Education (submitted)
  • Learning Analytics Acceptance Model
  • Learning Analytics Acceptance Model
  • Learning Analytics What to measure?Measurement, collection, analysis, and reporting of data about learners and their contexts
  • Learning Analytics forCommunity of Inquiry Effects of instructional interventionsExample: Role playing (invited expert and moderation) with explicit instructions how to contribute
  • Social Network Analytics inthe Community of Inquiry Just information sharingdoes not mean a central role
  • Social Network AnalyticsPerformance prediction based on joint course enrollmentExample: Degree, between centrality and closeness centrality explain ~46% of GPA
  • http://learningworksforkids.com/EF/metacognition.html
  • Social Learning Analytics for Self-regulated Workplace LearningSiadaty, M., Gašević, D., Jovanović, J., Milikić, N., Jeremić, N., Ali, L., Giljanović, A., Hatala, M., "Learn-B:Social Analytics-enabled Tool for Self-regulated Workplace Learning," In Proceedings of the 2ndInternational Conference on Learning Analytics and Knowledge, 2012, http://goo.gl/Vm8tv
  • Social Learning Analytics for Self-regulated Workplace LearningSiadaty, M., Gašević, D., Jovanović, J., Milikić, N., Jeremić, N., Ali, L., Giljanović, A., Hatala, M., "Learn-B:Social Analytics-enabled Tool for Self-regulated Workplace Learning," In Proceedings of the 2ndInternational Conference on Learning Analytics and Knowledge, 2012, http://goo.gl/Vm8tv
  • Social Learning Analytics for Self-regulated Workplace LearningSiadaty, M., Gašević, D., Jovanović, J., Milikić, N., Jeremić, N., Ali, L., Giljanović, A., Hatala, M., "Learn-B:Social Analytics-enabled Tool for Self-regulated Workplace Learning," In Proceedings of the 2ndInternational Conference on Learning Analytics and Knowledge, 2012, http://goo.gl/Vm8tv
  • DEPTHS DEsign Patterns Teaching Help System  Project-based learning  Self-regulation and community of inquiry http://op4l.fon.bg.ac.rs/op4l_services
  • DEPTHS
  • LOD-based Personal Learning Environments: Principles Integration of distributed and heterogeneous data sources, tools and servicesPrinciple 1: Quintessential for the realization of all other principles, and thus development of advanced PLEs, i.e., PLEsIntegration offering context-aware and personalized learning, as well as ubiquitous data access Open standards => application and device independence, long-term access to content and services, interoperabilityPrinciple 2: Openness Open source software => cost-effective customizations to the users’ needs, Open content => more diverse and constantly evolving and improving educational content The users’ ability to:Principle 3: - seamlessly access different tools/services that are part of their PLEs;Distributed Identity - pull together their profile data from those tools/services;Management - regulate the use of their data within tools/services that from their PLEs. Improved efficiency of user’s interactions with the environment through capturing and leveraging data aboutPrinciple 4: the users learning context;Context-awareness Improvements: higher quality of search results, proactive recommendations, mediation of communication/collaboration The ability to seamlessly “configure” a PLE for any given purpose (i.e., learning goal), by adding new and/orPrinciple 5: Modularity replacing existing content, tools and/or services Support for standardized and light-weight approaches for the development of dynamic (e-learning) mashups.Principle 6: Ubiquitous Seamless access to and integration of profile data, data about learning activities and learning resourcesdata access Ability to access and use relevant resources regardless of the system/tool/service the user is currently using The ‘user at the centre’ paradigm – student is responsible for managing his/her individual knowledge andPrinciple 7: competencesUser Centricity The learning system is the facilitator: it identifies the appropriate resources, adapts them to the user’s learning context, and suggests the most appropriate learning strategies Jeremić, Z., Jovanović, J., Gašević, D., "Personal Learning Environments on the Social Semantic Web," Semantic Web Journal, 2012 (in press), http://goo.gl/yaqQN
  • Learning Analytics How to analyze?Measurement, collection, analysis, and reporting of data about learners and their contexts
  • Measuring Cognitive Presence Text mining and linked dataA very similar text-mining problem is spam classification.Sure, sounds funny, but computing is a strange affair !
  • http://www.cs.waikato.ac.nz/ml/weka
  • Cognition and meta-cognitionDiscovering learning processes
  • http://www.processmining.org
  • SNAPPhttp://goo.gl/jtO3i http://nodexl.codeplex.com/
  • http://linkededucation.orgLinked Learning 2012 @ WWW 2012http://lile2012.linkededucation.org/
  • Learning analytics more meaningful & ubiquitouswith semantic technologies
  • Thank you!