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Aggregating social and usage datasets for learning analytics: data-oriented challenges

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Nikos ManouselisAuthors Info & Claims

LAK '13: Proceedings of the Third International Conference on Learning Analytics and Knowledge

Pages 245 - 249

https://doi.org/10.1145/2460296.2460345

Published: 08 April 2013 Publication History

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Abstract

Recent work has studied real-life social and usage datasets from educational applications, highlighting the opportunity to combine or merge them. It is expected that being able to put together different datasets from various applications will make it possible to support learning analytics of a much larger scale and across different contexts. We examine how this can be achieved from a practical perspective by carrying out a study that focuses on three real datasets. More specifically, we combine social data that has been collected from the users of three learning portals and reflect on how they should be handled. We start by studying the data types and formats that these portals use to represent and store social and usage data. Then we develop crosswalks between the different schemas, so that merged versions of the source datasets may be created. The results of this bottom-up, hands-on investigation reveal several interesting issues that need to be overcome before aggregated sets of social and usage data can be actually used to support learning analytics research or services.

References

[1]

Elias, T. Learning analytics - definitions, processes and potential, http://learninganalytics.net/learning-analyticsdefinitionsprocessespotential.pdf, 2012.

Google Scholar

[2]

Ferguson, R. The state of learning analytics in 2012: A review and future challenges. Tech. Rep. Technical Report KMI-12-01, Knowledge Media Institute, The Open University, UK, 2011.

Google Scholar

[3]

Massart, D., and Shulman, E. Interaction data exchange. D-Lib Magazine 21, 5/6 (2013).

Google Scholar

[4]

Scheffel, M., Niemann, K., Leony, D., Pardo, A., Schmitz, H.-C., Wolpers, M., and Delgado Kloos, C. Key action extraction for learning analytics. In EC-TEL (2012), A. Ravenscroft, S. N. Lindstaedt, C. Delgado Kloos, and D. Hernández Leo, Eds., vol. 7563 of Lecture Notes in Computer Science, Springer, pp. 320--333.

Digital Library

Google Scholar

[5]

Schmitz, H.-C., Wolpers, M., Kirschenmann, U., and Niemann, K. Dynamic ambient paradigms. In Paradigm Gems 2, A. Doe, Ed. Addison Wesley, 2005, pp. 223--233.

Google Scholar

[6]

Shane, D. Analytics to literacies: Emergent learning analytics to evaluate new literacies. Workshop on New Media, New Literacies, and New Forms of Learning, 2011.

Google Scholar

[7]

Verbert, K., Manouselis, N., Drachsler, H., and Duval, E. Dataset-driven research to support learning and knowledge analytics. Educational Technology & Society 15, 3 (2012), 133--148.

Google Scholar

[8]

Verbert, K., Manouselis, N., Ochoa, X., Wolpers, M., Drachsler, H., Bosnic, I., and Duval, E. Context-aware recommender systems for learning: A survey and future challenges. IEEE Transactions on Learning Technologies 99, PrePrints (2012).

Digital Library

Google Scholar

Cited By

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Nashed NLahoud CAbel M(2022)Ontology-based teacher-context data integration2022 IEEE/SICE International Symposium on System Integration (SII)10.1109/SII52469.2022.9708601(809-814)Online publication date: 9-Jan-2022
https://doi.org/10.1109/SII52469.2022.9708601
Shankar SRodríguez-Triana MPrieto LRuiz-Calleja AChejara P(2022)CDM4MMLA: Contextualized Data Model for MultiModal Learning AnalyticsThe Multimodal Learning Analytics Handbook10.1007/978-3-031-08076-0_9(205-229)Online publication date: 31-May-2022
https://doi.org/10.1007/978-3-031-08076-0_9
Bakhouyi ADehbi RBanane MTalea M(2020)A Semantic Web Solution for Enhancing the Interoperability of E-Learning Systems by Using Next Generation of SCORM SpecificationsAdvanced Intelligent Systems for Sustainable Development (AI2SD’2019)10.1007/978-3-030-36653-7_5(56-67)Online publication date: 3-Jan-2020
https://doi.org/10.1007/978-3-030-36653-7_5
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Index Terms

Aggregating social and usage datasets for learning analytics: data-oriented challenges
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

LAK '13: Proceedings of the Third International Conference on Learning Analytics and Knowledge

April 2013

300 pages

ISBN:9781450317856

DOI:10.1145/2460296

Editors:
Dan Suthers
University of Hawaii, USA
,
Katrien Verbert
Eindhoven University of Technology, The Netherlands
,
Erik Duval
University of Leuven, Belgium
,
Xavier Ochoa
Escuela Superior Politécnica del Litoral, Ecuador

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 April 2013

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Funding Sources

Sixth Framework Programme

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LAK '13

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SIGWEB
K.U. Leuven
SIGCHI

LAK '13: Third Conference on Learning Analytics and Knowledge

April 8 - 13, 2013

Leuven, Belgium

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LAK '13 Paper Acceptance Rate 16 of 58 submissions, 28%;

Overall Acceptance Rate 236 of 782 submissions, 30%

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Cited By

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Nashed NLahoud CAbel M(2022)Ontology-based teacher-context data integration2022 IEEE/SICE International Symposium on System Integration (SII)10.1109/SII52469.2022.9708601(809-814)Online publication date: 9-Jan-2022
https://doi.org/10.1109/SII52469.2022.9708601
Shankar SRodríguez-Triana MPrieto LRuiz-Calleja AChejara P(2022)CDM4MMLA: Contextualized Data Model for MultiModal Learning AnalyticsThe Multimodal Learning Analytics Handbook10.1007/978-3-031-08076-0_9(205-229)Online publication date: 31-May-2022
https://doi.org/10.1007/978-3-031-08076-0_9
Bakhouyi ADehbi RBanane MTalea M(2020)A Semantic Web Solution for Enhancing the Interoperability of E-Learning Systems by Using Next Generation of SCORM SpecificationsAdvanced Intelligent Systems for Sustainable Development (AI2SD’2019)10.1007/978-3-030-36653-7_5(56-67)Online publication date: 3-Jan-2020
https://doi.org/10.1007/978-3-030-36653-7_5
Bakhouyi ADehbi RTalea M(2019)Toward an Adaptive Architecture for Integrating Mobile Apps with LMS using Next Generation of SCORM2019 2nd International Conference on Computer Applications & Information Security (ICCAIS)10.1109/CAIS.2019.8769575(1-7)Online publication date: May-2019
https://doi.org/10.1109/CAIS.2019.8769575
Sedkaoui SKhelfaoui M(2019)Understand, develop and enhance the learning process with big dataInformation Discovery and Delivery10.1108/IDD-09-2018-004347:1(2-16)Online publication date: 1-Mar-2019
https://doi.org/10.1108/IDD-09-2018-0043
Bakhouyi ADehbi RTalea M(2019)A New Web Service Architecture for Enhancing the Interoperability of LMS and Mobile Applications Using the Next Generation of SCORMAdvanced Intelligent Systems for Sustainable Development (AI2SD’2018)10.1007/978-3-030-11928-7_65(719-726)Online publication date: 7-Mar-2019
https://doi.org/10.1007/978-3-030-11928-7_65
Askinadze AConrad S(2017)A Web Service Architecture for Tracking and Analyzing Data from Distributed E-Learning Environments2017 IEEE 26th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)10.1109/WETICE.2017.52(208-213)Online publication date: Jul-2017
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Pirkkalainen HJokinen JPawlowski J(2014)Understanding Social OER Environments—A Quantitative Study on Factors Influencing the Motivation to Share and CollaborateIEEE Transactions on Learning Technologies10.1109/TLT.2014.23239707:4(388-400)Online publication date: 1-Oct-2014
https://doi.org/10.1109/TLT.2014.2323970
Rayon AGuenaga MNunez A(2014)Ensuring the integrity and interoperability of educational usage and social data through Caliper framework to support competency-assessment2014 IEEE Frontiers in Education Conference (FIE) Proceedings10.1109/FIE.2014.7044448(1-9)Online publication date: Oct-2014
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Dietze SDrachsler HGiordano D(2014)A Survey on Linked Data and the Social Web as Facilitators for TEL Recommender SystemsRecommender Systems for Technology Enhanced Learning10.1007/978-1-4939-0530-0_3(47-75)Online publication date: 22-Mar-2014
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