{"id":31,"date":"2024-04-04T05:25:06","date_gmt":"2024-04-04T02:25:06","guid":{"rendered":"https:\/\/sisu.ut.ee\/icalt2020\/track-6-big-data-education-and-learning-analytics-bdela\/"},"modified":"2024-04-04T05:26:00","modified_gmt":"2024-04-04T02:26:00","slug":"track-6-big-data-education-and-learning-analytics-bdela","status":"publish","type":"page","link":"https:\/\/sisu.ut.ee\/icalt2020\/track-6-big-data-education-and-learning-analytics-bdela\/","title":{"rendered":"Track 6. Big Data in Education and Learning Analytics (BDELA)"},"content":{"rendered":"

\n\tTrack Program Chairs<\/strong>
\n<\/h2>\n
\n
\n\t\t\"\"\n

\n\t\t\tVive Kumar<\/strong>
\n\t\t<\/h4>\n
\n\t\t\tAthabasca University, Canada
\n\t\t<\/h5>\n
\n\t\t\t(CO-ORDINATOR)
\n\t\t<\/h5>\n<\/div>\n
\n\t\t\"\"\n

\n\t\t\tAbelardo Pardo<\/strong>
\n\t\t<\/h4>\n
\n\t\t\tThe University of Sydney, Australia
\n\t\t<\/h5>\n<\/div>\n
\n\t\t\"\"\n

\n\t\t\tDirk Ifenthaler<\/strong>
\n\t\t<\/h4>\n
\n\t\t\tUniversity of Mannheim, Germany & Curtin University, Australia
\n\t\t<\/h5>\n<\/div>\n
\n\t\t\"\"\n

\n\t\t\tXiao Hu<\/strong>
\n\t\t<\/h4>\n
\n\t\t\tThe University of Honk Kong, Hong Kong
\n\t\t<\/h5>\n<\/div>\n<\/div>\n
\n
\n\t\t\"\"\n

\n\t\t\tDemetrios G Sampson<\/strong>
\n\t\t<\/h4>\n
\n\t\t\tUniversity of Piraeus, Greece & Curtin University, Australia
\n\t\t<\/h5>\n<\/div>\n
\n\t\t\"\"\n

\n\t\t\tStephen Yang<\/strong>
\n\t\t<\/h4>\n
\n\t\t\tNational Central University, Taiwan
\n\t\t<\/h5>\n<\/div>\n
\n\t\t\"\"\n

\n\t\t\tBernardo Pereira<\/strong>
\n\t\t<\/h4>\n
\n\t\t\tAustralian National University, Australia
\n\t\t<\/h5>\n<\/div>\n<\/div>\n

\n\tTrack description and topics of interest<\/strong>
\n<\/h2>\n

\n\tThe analysis and discovery of relations characterizing human learning, and contextual factors that influence these relations have been one of the contemporary and critical global challenges faced by researchers in a number of areas, particularly in Education, Psychology, Sociology, Information Systems, and Computing. These relations typically concern learners\u2019 achievements and the overall learning experience, and the effectiveness of the learning context. Be it the assessment marks distribution in a classroom context or the mined pattern of best practices in an apprenticeship context, analysis and discovery have always addressed the elusive causal question about the need to best serve learners\u2019 learning efficiency, learning effectiveness, as well as the overall learning experience, and the need to make informed choices on a learning context\u2019s instructional effectiveness.\n<\/p>\n

\n\tSignificant advances have been made in a number of areas from educational psychology to artificial intelligence in education, which explored factors contributing to learners\u2019 proactive role in the learning process and instructional effectiveness. With the advent of new technologies such as eye-tracking, activity monitoring, video analysis, content analysis, sentiment analysis, immersive worlds, social network analysis and interaction analysis, one could study these factors in a data-intensive context. This very notion is what is currently being explored at the intersection of big data and learning analytics, which includes related areas such as learning process analytics, institutional effectiveness, academic analytics, web analytics and information visualization.\n<\/p>\n

\n\tBDELA will explore monitoring of learner progress and tracing of skill development of individual learners as well as learning groups, both within and across programs and institutions. It will discuss issues concerning evaluation of achievements resulting from institutional educational practices to gauge alignment with strategic plans and alignment of governmental strategies. It will examine assessment frameworks of academic productivity to measure impact of teaching. It will discuss concerns such as quality of instruction, attrition, and measurement of curricular outcomes using big data and associated methods and techniques as the premise\n<\/p>\n

\n\t\u00a0\n<\/p>\n

\n\tTopics:\n<\/p>\n

    \n
  • \n\t\tBig data theory, science and technology for education and learning\n
      \n
    • \n\t\t\t\tanalysis of unstructured and semi-structured data\n\t\t\t<\/li>\n
    • \n\t\t\t\tsecurity, privacy and ethics of big data analytics\n\t\t\t<\/li>\n
    • \n\t\t\t\tveracity in big data\n\t\t\t<\/li>\n
    • \n\t\t\t\tscalability of machine learning and data mining algorithms for big data\n\t\t\t<\/li>\n
    • \n\t\t\t\tcomputing infrastructure for big data \u2013 cloud, grid, autonomic, stream, mobile, high performance computing\n\t\t\t<\/li>\n
    • \n\t\t\t\tsearch in big data\n\t\t\t<\/li>\n
    • \n\t\t\t\tartificial intelligence in big data analytics\n\t\t\t<\/li>\n
    • \n\t\t\t\tuncertainty handling in big data\n\t\t\t<\/li>\n
    • \n\t\t\t\tIoT and big\u00a0data analytics\n\t\t\t<\/li>\n<\/ul>\n<\/li>\n
    • \n\t\tApplications of big data in education and learning analytics\n
        \n
      • \n\t\t\t\tdetecting student\u2019s approach to learning\n\t\t\t<\/li>\n
      • \n\t\t\t\tanalytics in academic administration\n\t\t\t<\/li>\n
      • \n\t\t\t\tdata analytics in complex training\n\t\t\t<\/li>\n
      • \n\t\t\t\tgaming analytics and sports analytics\n\t\t\t<\/li>\n
      • \n\t\t\t\tevidence-driven instruction in inter and individual disciplines\n\t\t\t<\/li>\n
      • \n\t\t\t\tbig data and educational technology\n\t\t\t<\/li>\n
      • \n\t\t\t\tanalytics in academic strategic planning\n\t\t\t<\/li>\n
      • \n\t\t\t\tcultural analytics\n\t\t\t<\/li>\n
      • \n\t\t\t\tlarge-scale social networks\n\t\t\t<\/li>\n
      • \n\t\t\t\tdata literacy\n\t\t\t<\/li>\n
      • \n\t\t\t\ttechnological literacy and analytics\n\t\t\t<\/li>\n
      • \n\t\t\t\thuman literacy and analytics\n\t\t\t<\/li>\n<\/ul>\n<\/li>\n
      • \n\t\tTechniques of big data in education, knowledge and learning analytics\n
          \n
        • \n\t\t\t\tevidence-driven mixed-initiative learning\n\t\t\t<\/li>\n
        • \n\t\t\t\tdata-intensive learning and instructional design\n\t\t\t<\/li>\n
        • \n\t\t\t\temerging standards in learning analytics\n\t\t\t<\/li>\n
        • \n\t\t\t\tsentiment analysis\n\t\t\t<\/li>\n
        • \n\t\t\t\tlarge-scale productivity analysis\n\t\t\t<\/li>\n
        • \n\t\t\t\tbig data infrastructure for academic institutions and SMEs\n\t\t\t<\/li>\n
        • \n\t\t\t\tscalable knowledge management\n\t\t\t<\/li>\n
        • \n\t\t\t\tobservational research methods for analytics\n\t\t\t<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"

          Track Program Chairs Vive Kumar Athabasca University, Canada (CO-ORDINATOR) Abelardo Pardo The University of Sydney, Australia Dirk Ifenthaler University of Mannheim, Germany & Curtin University, Australia Xiao Hu The University of Honk Kong, Hong Kong Demetrios G Sampson University of …<\/p>\n","protected":false},"author":30,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"class_list":["post-31","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/sisu.ut.ee\/icalt2020\/wp-json\/wp\/v2\/pages\/31","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sisu.ut.ee\/icalt2020\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sisu.ut.ee\/icalt2020\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sisu.ut.ee\/icalt2020\/wp-json\/wp\/v2\/users\/30"}],"replies":[{"embeddable":true,"href":"https:\/\/sisu.ut.ee\/icalt2020\/wp-json\/wp\/v2\/comments?post=31"}],"version-history":[{"count":1,"href":"https:\/\/sisu.ut.ee\/icalt2020\/wp-json\/wp\/v2\/pages\/31\/revisions"}],"predecessor-version":[{"id":220,"href":"https:\/\/sisu.ut.ee\/icalt2020\/wp-json\/wp\/v2\/pages\/31\/revisions\/220"}],"wp:attachment":[{"href":"https:\/\/sisu.ut.ee\/icalt2020\/wp-json\/wp\/v2\/media?parent=31"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}