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Educational monitoring tool based on faceted browsing and data portraits

Published:29 April 2012Publication History

ABSTRACT

Due to the idiosyncrasy of online education, students may become disoriented, frustrated or confused if they do not receive the support, feedback or guidance needed to be successful. To avoid this, the role of teachers is essential. In this regard, instructors should be facilitators who guide students throughout the teaching-learning process and arrange meaningful learner-centered experiences. However, unlike face-to-face classes, teachers have difficulty in monitoring their learners in an online environment, since a lot of learning management systems provide faculty with student tracking data in a poor tabular format that is difficult to understand. In order to overcome this drawback, this paper presents a novel graphical educational monitoring tool based on faceted browsing that helps instructors to gain an insight into their classrooms' performance. Moreover, this tool depicts information of each individual student by using a data portrait. Thanks to this monitoring tool, teachers can, on the one hand, track their students during the teaching-learning process and, on the other, detect potential problems in time.

References

  1. R. S. Baker and K. Yacef. The State of Educational Data Mining in 2009: A Review and Future Visions. JEDM - Journal of Educational Data Mining (ISSN 2157--2100), 1(1):3--17, Oct. 2009.Google ScholarGoogle Scholar
  2. A. Bakharia and S. Dawson. Snapp: a bird's-eye view of temporal participant interaction. In Proceedings of the 1st International Conference on Learning Analytics and Knowledge, LAK '11, pages 168--173, New York, NY, USA, 2011. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. S. Card, J. Mackinlay, and B. Shneiderman. Readings in information visualization: using vision to think. The Morgan Kaufmann series in interactive technologies. Morgan Kaufmann Publishers, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. N. Dabbagh and A. Kitsantas. Supporting self-regulation in student-centered web-based learning environments. International Journal on E-Learning, 3(1):40--47, 2004.Google ScholarGoogle Scholar
  5. J. Donath, A. Dragulescu, A. Zinman, F. Viégas, and R. Xiong. Data portraits. In SIGGRAPH '10: ACM SIGGRAPH 2010 Art Gallery, pages 375--383, New York, NY, USA, 2010. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. A. C. Dragulescu. Data Portraits: Aesthetics and Algorithms. PhD thesis, Massachusetts Institute of Technology, 2009.Google ScholarGoogle Scholar
  7. D. García-Solórzano, G. Cobo, E. Santamaria, J. A. Morán, and J. Melenchón. Representation of a course structure focused on activities using information visualization techniques. In ICALT, pages 443--445, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. N. Hara and R. Kling. Students' distress with a web-based distance education course. Information, Communication and Society, 3(4):557--579, 2000.Google ScholarGoogle ScholarCross RefCross Ref
  9. J. Hardy, S. Bates, J. Hill, and M. Antonioletti. Tracking and Visualisation of Student Use of Online Learning Materials in a Large Undergraduate Course, volume 4823 of Lecture Notes in Computer Science, pages 464--474. Springer Berlin/Heidelberg, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. R. Hijón and Ángel Velázquez. E-learning platforms analysis and development of students tracking functionality. In E. Pearson and P. Bohman, editors, Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2006, pages 2823--2828, Chesapeake, VA, June 2006. AACE.Google ScholarGoogle Scholar
  11. R. Hijón-Neira and J. A. Velázquez-Iturbide. How to improve assessment of learning and performance through interactive visualization. In ICALT '08: Proceedings of the 2008 Eighth IEEE International Conference on Advanced Learning Technologies, pages 472--476, Washington, DC, USA, 2008. IEEE Computer Society. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. J. L. Hung and K. Zhang. Revealing Online Learning Behaviors and Activity Patterns and Making Predictions with Data Mining Techniques in Online Teaching. Journal of Online Learning and Teaching, 4(4):428--437, 2008.Google ScholarGoogle Scholar
  13. I. Icke and E. Sklar. A visualization tool for student assessments data. In From Theory to Practice: Design, Vision and Visualization Workshop. IEEE VisWeek (2008), 2008.Google ScholarGoogle Scholar
  14. A. A. Juan, T. Daradoumis, J. Faulin, and F. Xhafa. Developing an information system for monitoring student's activity in online collaborative learning. In CISIS '08: Proceedings of the 2008 International Conference on Complex, Intelligent and Software Intensive Systems, pages 270--275, Washington, DC, USA, 2008. IEEE Computer Society. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. D. A. Keim, W. Müller, and H. Schumann. Visual data mining. In D. Fellner and R. Scopigno, editors, STAR Proceedings of Eurographics 2002, pages 49--68. Eurographics Association, 2002.Google ScholarGoogle Scholar
  16. J. Koren, Y. Zhang, and X. Liu. Personalized interactive faceted search. In Proceeding of the 17th international conference on World Wide Web, WWW '08, pages 477--486, New York, NY, USA, 2008. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. E. Kosba, V. Dimitrova, and R. D. Boyle. Using Student and Group Models to Support Teachers in Web-Based Distance Education. In L. Ardissono, P. Brna, and A. Mitrovic, editors, UM2005 User Modeling: Proceedings of the 10th International Conference, pages 124--133, Edinburgh, Scotland, 2005. Springer Verlag. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. J. Mackinlay. Automating the design of graphical presentations of relational information. ACM Trans. Graph., 5(2):110--141, 1986. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. D. Maor. The teacher's role in developing interaction and reflection in an online learning community. Educational Media International, 40(1/2):127--138, 2003.Google ScholarGoogle ScholarCross RefCross Ref
  20. R. Mazza and V. Dimitrova. Informing the design of a course data visualisator: an empirical study. In 5th International Conference on New Educational Environments (ICNEE 2003), May 2003.Google ScholarGoogle Scholar
  21. R. Mazza and V. Dimitrova. Visualising student tracking data to support instructors in web-based distance education. In 13th International World Wide Web Conference - Educational Track, pages 154--161. ACM Press, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. R. Mazza and C. Milani. GISMO: a graphical interactive student monitoring tool for course management systems. In T. E. L. '04 Technology Enhanced Learning '04 International Conference. Milan, pages 18--19, 2004.Google ScholarGoogle Scholar
  23. C. McInnis, R. James, and C. McNaught. First year on campus. a commissioned project of the committee for the advancement of university teaching. Technical report, Canberra: Australian Government Publishing Service, 1995.Google ScholarGoogle Scholar
  24. A. Merceron and K. Yacef. Interestingness measures for associations rules in educational data. In EDM'08, pages 57--66, 2008.Google ScholarGoogle Scholar
  25. T. J. F. Mitchell, S. Y. Chen, and R. D. Macredie. The relationship between web enjoyment and student perceptions and learning using a web-based tutorial. Learning, Media and Technology, 30(1):27--40, 2005.Google ScholarGoogle ScholarCross RefCross Ref
  26. OECD. Learning for tomorrows world | first results from pisa 2003. Technical report, Organisation for Economic Co-operation and Development (OECD), 2004.Google ScholarGoogle Scholar
  27. R. Pirrone, V. Cannella, and G. Russo. A map-based visualization tool to support tutors in e-learning 2.0. In HSI'09: Proceedings of the 2nd conference on Human System Interactions, pages 482--487, Piscataway, NJ, USA, 2009. IEEE Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. C. Romero and S. Ventura. Educational data mining: A survey from 1995 to 2005. Expert Syst. Appl., 33(1):135--146, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. P. C. Salomon. Effective and responsible teaching: The new synthesis, chapter The changing role of the teacher: From information transmitter to orchestrator of learning, pages 35--49. Jossey-Bass, San Francisco, CA, 1992.Google ScholarGoogle Scholar
  30. J. Taylor. Teaching and learning online: the workers, the lurkers and the shirkers. In 2nd Conference on Research in Distance and Adult Learning in Asia, CRIDALA 2002, 5--7 June 2002.Google ScholarGoogle Scholar
  31. F. B. Viégas. Newsgroup crowds and authorlines: Visualizing the activity of individuals in conversational cyberspaces. In In: Proceedings of the 37th Hawaii International Conference on System Sciences, IEEE, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. W. R. Watson and S. L. Watson. An Argument for Clarity: What are Learning Management Systems, What are They Not, and What Should They Become? TechTrends, 51(2):28--34, Mar. 2007.Google ScholarGoogle ScholarCross RefCross Ref
  33. R. Xiong and J. Donath. Peoplegarden: creating data portraits for users. In UIST '99: Proceedings of the 12th annual ACM symposium on User interface software and technology, pages 37--44, New York, NY, USA, 1999. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. K.-P. Yee, K. Swearingen, K. Li, and M. Hearst. Faceted metadata for image search and browsing. In Proceedings of the SIGCHI conference on Human factors in computing systems, CHI '03, pages 401--408, New York, NY, USA, 2003. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. H. Zhang, K. Almeroth, A. Knight, M. Bulger, and R. Mayer. Moodog: Tracking students' online learning activities. In Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2007, pages 4415--4422, 2007.Google ScholarGoogle Scholar
  36. W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld. Face recognition: A literature survey. ACM Comput. Surv., 35:399--458, December 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. M. Zorrilla, D. García, and E. Álvarez. A decision support system to improve e-learning environments. In Proceedings of the 2010 EDBT/ICDT Workshops, EDBT '10, pages 11:1--11:8, New York, NY, USA, 2010. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library

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            cover image ACM Conferences
            LAK '12: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge
            April 2012
            282 pages
            ISBN:9781450311113
            DOI:10.1145/2330601

            Copyright © 2012 ACM

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            Publication History

            • Published: 29 April 2012

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