Construction of learning analytics system

MA Xiao-ling; XING Wan-li; FENG Xiang; WU Yong-he

Issue 2

Jun. 2014

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Article Navigation > Journal of East China Normal University (Natural Sciences) > 2014 > (2): 1-19, 39

MA Xiao-ling, XING Wan-li, FENG Xiang, WU Yong-he. Construction of learning analytics system[J]. Journal of East China Normal University (Natural Sciences), 2014, (2): 1-19, 39.

Citation:

MA Xiao-ling, XING Wan-li, FENG Xiang, WU Yong-he. Construction of learning analytics system[J]. Journal of East China Normal University (Natural Sciences), 2014, (2): 1-19, 39.

MA Xiao-ling, XING Wan-li, FENG Xiang, WU Yong-he. Construction of learning analytics system[J]. Journal of East China Normal University (Natural Sciences), 2014, (2): 1-19, 39.

Citation:

MA Xiao-ling, XING Wan-li, FENG Xiang, WU Yong-he. Construction of learning analytics system[J]. Journal of East China Normal University (Natural Sciences), 2014, (2): 1-19, 39.

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Construction of learning analytics system

1.
Department of Information, East China Normal University, Shanghai 200062, China;
2.
School of Information Science and Learning Technologies, University of Missouri-Columbia, MO 65201, USA;
3.
School of Open Education and Learning &Shanghai Engineering Research Center of Digital Education Equipment, East China Normal University, Shanghai 200062, China

Received Date: 2013-11-01
Rev Recd Date: 2014-01-01
Publish Date: 2014-03-25

Abstract

Abstract

Learning Analytics (LA) has been applied to various learning environments. However, a complete and clear learning data system is needed. This study tried to provide an open and comprehensive LA system. Firstly, summary of recent researches and developments in this field was given, a number of important concepts were defined, and the interaction evolution of time, space and data of LAS was studied. Then the learning analysis system Universe of Discourse (LASUoD) including formalized modeling of LAS information space model ［R,CR,MT,A,W,C］ was analyzed. In order to achieve interoperability of LAS applications, the frame of learning analytics system ontology (LASO), data format of LA object and the profiles of LAS standards were proposed. Finally an LA application based on open source SAKAI was demonstrated.
- learning analytics object,
- learning analytics system,
- formal modeling,
- Information space model,
- system architecture,
- analysis of dataflow,
- learning analytics ontology,
- learning analytics standard

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References(1)

References

[1]

［1］ FERGUSON R. The state of learning analytics in 2012: A review and future challenges［R］. Knowledge Media Institute, Technical Report. KMI-2012-01.

［2］ DAWSON S, Heathcote L, et al. Harnessing ICT potential: The adoption and analysis of ICT systems for enhancing the student learning experience［J］. International Journal of Educational Management,2010, 24(2), 116-128.

［3］ The New Media Consortium. The Horizon Report 2013 Higher Education Edition［EB/OL］. ［2013-8-28］. http://net.educause.edu/ir/library/pdf/HR2013.pdf.

［4］吴永和,陈丹,马晓玲,等. 学习分析——教育信息化新浪潮［J］. 远程教育杂志, 2013,313: 9-19. 

［5］ SIEMENS G. Knewton-the future of education? ［EB/OL］．［2013-8-28］. http: // www.learninganalytics. net .

［6］ BROWN M. Learning analytics: the coming third wave［EB/OL］. ［2013-8-28］. http://net.educause.edu/ir/library/pdf/ELIB1101.pdf. 

［7］ PARDOS Z A, BAKER R S, SAN PEDRO M O, et al. Affective states and state tests: Investigating how affect throughout the school year predicts end of year learning outcomes［C］//Poreedings of the Third International Conference on Learning Analytics and Knowledge (pp. 117-124). ACM.

［8］ KIZILCEC R F, PIECH SCHNEIDERconstructing disengagement: analyzing learner subpopulations in massive open online courses［C］//Proceedings of the Third International Conference on Learning Analytics and Knowledge ACM, 2013: 170-179.

［9］ LONN S, KRUMM A E, WADDINGTON R J, et al. Bridging the gap from knowledge to action: Putting analytics in the hands of academic advisors［C］. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge. ACM, 2012: 184-187.

［10］ GARCA-SOLRZANO D, MORn J A, COBO G, et al. Educational monitoring tool based on faceted browsing and data portraits［C］. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge. ACM, 2012: 170-178.

［11］ GUNNARSSON B L, ALTERMAN R. Understanding promotions in a case study of student blogging［C］//Proceedings of the Third International Conference on Learning Analytics and Knowledge. ACM, 2013: 57-65.

［12］ SOUTHAVILAY V, YACEF K, REIMANN P, et al. Analysis of collaborative writing processes using revision maps and probabilistic topic models［C］//Proceedings of the Third International Conference on Learning Analytics and Knowledge. ACM, 2013: 38-47.

［13］ FERGUSON R, SHUM S B. Social learning analytics: five approaches［C］//Proceedings of the 2nd International Conference on Learning Analytics and Knowledge. ACM, 2012: 23-33.

［14］ BLIKSTEIN P. Multimodal learning analytics［C］//Proceedings of the Third International Conference on Learning Analytics and Knowledge. ACM, 2013: 102-106.

［15］ CLOW D. The learning analytics cycle: closing the loop effectively［C］//Proceedings of the 2nd International Conference on Learning Analytics & Knowledge. Vancouver, BC: ACM, 2012: 134-138.

［16］ GRELLER W, DRACHSLER H. Translating learning into numbers: A generic framework for learning analytics［J］. Educational Technology & Society, 2012, 15(3): 42-57.

［17］ SIEMENS G, GASEVIC D, HAYTHORNTHWAITE C, et al. Open Learning Analytics: an integrated & modularized platform ［R］. Eney: Solar, 2011.

［18］ NIEMNN K, WOLPERS M, STOITSIS G, et al. Aggregating social and usage datasets for learning analytics: data-oriented challenges［C］//Proceedings of the Third International Conference on Learning Analytics and Knowledge. ACM, 2013: 245-249.

［19］ ARNOLD K E, PISTILLI M D. Course signals at Purdue: Using learning analytics to increase student success［C］//Proceedings of the 2nd International Conference on Learning Analytics and Knowledge. ACM, 2012: 267-270.

［20］ BRAMUCCI R, GASTON J. Sherpa: increasing student success with a recommendation engine［C］. LAK, 2012: 82-83.

［21］吴永和.学习资源服务生态环境构建的研究［D］.上海:华东师范大学, 2009.

［22］ ZHU Z. New Challenges for Developing e-Learning Technology Standards［C］//14th International Conference on Information Technology-Learning, Education and Training & ISO/IEC JTC1 SC36 Open Forum 2006,Wuhan, China, 2006.

［23］ WU Y, YU P, MA X, et al. Research on the Construction of a Learning Resource Service Eco-System Universe of Discourse［C］//International Conference on E-Business and E-Government. IEEE Xplore, 2010: 5506-5512.

［24］ RITCHEY T. Analysis and synthesis: on scientific method–based on a study by Bernhard Riemann［J］. Systems Research, 1991, 8(4): 21-41.

［25］ BIENKOWSKI M, FENG M, et al. Enhancing teaching and learning through educational data mining and learning analytics: An issue brief［R］. Washington, DC: SRI International, 2012.

［26］ SIEMENS G, LONG P. Penetrating the Fog: Analytics in learning and education［J］. EDUCAUSE Review, 2011, 46(4): 30-32.

［27］ VERBERT K, MANOUSELIS N, DRACHSLER H, et al. Dataset-driven research to support learning and knowledge analytics［J］. Educational Technology & Society, 2012,15(3): 133-148.

［28］ DATATEL［EB/OL］. ［2013-8-10］. http://www.teleurope.eu/pg/groups/9405/datatel/.

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