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E-learning personalization based on itineraries and long-term navigational behavior

Published: 19 May 2004 Publication History

Abstract

In this paper we describe a practical framework for studying then a navigational behavior of the users of an e-learning environment integrated in a virtual campus. The students navigate through the web based virtual campus interacting with learning resources which are structured following the SCORM e-learning standard. Our main goal is to design a usage mining tool for analyzing such user navigational behavior and for extracting relevant information that can be used to validate several aspects related to virtual campus design and usability but also to determine the optimal scheduling for each course depending on user profile. We intend to extend these quencing capabilities of the SCORM standard to include the concept of recommended itinerary, by combining teachers expertise with learned experience acquired by system usage analysis.

References

[1]
ADL. Sharable content object reference model (SCORM) 2004. Technical report, Advanced Distributed Learning, 2004.
[2]
E. Chi, P. Pirolli, and J. Pitkow. The scent of a site: A system for analyzing and predicting information scent, usage, and usability of a web site. In Proc. of ACM CHI 2000, pages 161--168, Amsterdam, Netherlands, 2000.
[3]
C. Clifton and V. Estivill-Castro, editors. Proc. of the ICDM 2002, Workshop on Privacy, Security and Data Mining, volume 14, Maebashi City, Japan, 2002. ACS.
[4]
J. Srivastava, R. Cooley, M. Deshpande, and P.-N. Tan. Web usage mining: discovery and applications of usage patterns from web data. In ACM SIGKDD Explorations, volume 1(2), pages 12--23, 2000.

Cited By

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  • (2020)Using Learning Management System Activity Data to Predict Student Performance in Face-to-Face CoursesInternational Journal of Mobile and Blended Learning10.4018/IJMBL.202007010212:3(20-31)Online publication date: 1-Jul-2020
  • (2019)Educational Data Mining and Learning Analytics for 21stcentury higher education: A Review and SynthesisTelematics and Informatics10.1016/j.tele.2019.01.007Online publication date: Jan-2019
  • (2018)Digging Deep Inside: An Extended Analysis of SCHOLAT E-Learning DataHuman Centered Computing10.1007/978-3-319-74521-3_44(410-421)Online publication date: 23-Jan-2018
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  1. E-learning personalization based on itineraries and long-term navigational behavior

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      cover image ACM Conferences
      WWW Alt. '04: Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
      May 2004
      532 pages
      ISBN:1581139128
      DOI:10.1145/1013367
      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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      New York, NY, United States

      Publication History

      Published: 19 May 2004

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      Author Tags

      1. SCORM
      2. data mining
      3. e-learning
      4. navigational patterns
      5. personalization

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      Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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      View all
      • (2020)Using Learning Management System Activity Data to Predict Student Performance in Face-to-Face CoursesInternational Journal of Mobile and Blended Learning10.4018/IJMBL.202007010212:3(20-31)Online publication date: 1-Jul-2020
      • (2019)Educational Data Mining and Learning Analytics for 21stcentury higher education: A Review and SynthesisTelematics and Informatics10.1016/j.tele.2019.01.007Online publication date: Jan-2019
      • (2018)Digging Deep Inside: An Extended Analysis of SCHOLAT E-Learning DataHuman Centered Computing10.1007/978-3-319-74521-3_44(410-421)Online publication date: 23-Jan-2018
      • (2017)Integration of data mining techniques in e-learning systemsProceedings of the 2nd international Conference on Big Data, Cloud and Applications10.1145/3090354.3090453(1-4)Online publication date: 29-Mar-2017
      • (2016)Methodology of Predictive Modeling of Students’ Behavior in Virtual Learning EnvironmentFormative Assessment, Learning Data Analytics and Gamification10.1016/B978-0-12-803637-2.00010-5(187-216)Online publication date: 2016
      • (2015)Visit rate analysis of course activities: Case study2015 13th International Conference on Emerging eLearning Technologies and Applications (ICETA)10.1109/ICETA.2015.7558507(1-6)Online publication date: Nov-2015
      • (2014)Improving Personalization In E-Learning SystemsInternational Journal of Technology and Educational Marketing10.4018/ijtem.20140701074:2(75-84)Online publication date: 1-Jul-2014
      • (2013)Navigation and Visualisation Techniques in eLearning and Internet ResearchDigital Literacy10.4018/978-1-4666-1852-7.ch033(636-667)Online publication date: 2013
      • (2013)A study of the impact of task complexity and interface design on e-learning task adaptationsProceedings of the 11th Asia Pacific Conference on Computer Human Interaction10.1145/2525194.2525203(19-27)Online publication date: 24-Sep-2013
      • (2013)Intelligent Decision-Making Support within the E-Learning ProcessIntelligent and Adaptive Educational-Learning Systems10.1007/978-3-642-30171-1_20(497-521)Online publication date: 2013
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