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

Published: 08 April 2013 Publication History
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    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.

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    Cited By

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    • (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
    • (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
    • (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
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    1. Aggregating social and usage datasets for learning analytics: data-oriented challenges

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        cover image ACM Conferences
        LAK '13: Proceedings of the Third International Conference on Learning Analytics and Knowledge
        April 2013
        300 pages
        ISBN:9781450317856
        DOI:10.1145/2460296
        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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        Publication History

        Published: 08 April 2013

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        Author Tags

        1. data-driven analysis
        2. dataset
        3. education
        4. experimental investigation
        5. usage data formats

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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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        • (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
        • (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
        • (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
        • (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
        • (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
        • (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
        • (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: Jun-2017
        • (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
        • (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
        • (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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