Diagnostic Feedback by Snap-drift Question Response Grouping

Part of book or chapter of book English OPEN
Lee, Sin Wee ; Palmer-Brown, Dominic ; Draganova, Chrisina (2008)
  • Publisher: World Scientific and Engineering Academy and Society
  • Subject:
    acm: ComputingMilieux_COMPUTERSANDEDUCATION

This work develops a method for incorporation into an on-line system to provide carefully targeted guidance and feedback to students. The student answers on-line multiple choice questions on a selected topic, and their responses are sent to a Snap-Drift neural network trained with responses from a past students. Snap-drift is able to categorise the learner's responses as having a significant level of similarity with a subset of the students it has previously categorised. Each category is associated with feedback composed by the lecturer on the basis of the level of understanding and prevalent misconceptions of that category-group of students. In this way the feedback addresses the level of knowledge of the individual and guides them towards a greater understanding of particular concepts. The feedback is concept-based rather than tied to any particular question, and so the learner is encouraged to retake the same test and receives different feedback depending on their evolving state of knowledge.
  • References (12)
    12 references, page 1 of 2

    [1] Lee, S. W., Palmer-Brown, D., Tepper, J., and Roadknight, C. M., Snap-Drift: Real-time, Performance-guided Learning, Proceedings of the International Joint Conference on Neural Networks (IJCNN'2003), 2003, Volume 2, pp. 1412 - 1416.

    [2] Lee, S. W., Palmer-Brown, D., and Roadknight, C. M., Performance-guided Neural Network for Rapidly Self-Organising Active Network Management, Neurocomputing, Volume 61, pp. 5 - 20.

    [3] Donelan, H., Pattinson, Palmer-Brown, D., and Lee, S. W., The Analysis of Network Manager's Behaviour using a Self-Organising Neural Networks, Proceedings of The 18th European Simulations Multiconference, 2004, pp. 111 - 116.

    [4] Lee, S. W., and Palmer-Brown, D., Phrase Recognition using Snap-Drift Learning Algorithm, Proceedings of The International Joint Conference on Neural Networks, 2005, Volume 1, pp. 588-592.

    [5] R. Garside, G. Leech and T. Varadi, Manual of Information to Accompany the Lancaster Parsed Corpus: Department of English, University of Oslo, 1987.

    [6] Lee, S. W., and Palmer-Brown, D., Phonetic Feature Discovery in Speech using Snap-Drift, Proceedings of International Conference on Artificial Neural Networks (ICANN'2006), S. Kollias et al. (Eds.): ICANN 2006, Part II, LNCS 4132, pp. 952 - 962.

    [7] Brown G., J. Bull and M. Pendlebury, Assessing Students Learning in Higher Education, Routledge, London, 1997

    [8] Higgins E, Tatham L, Exploring the potential of Multiple Choice Questions in Assessment, Learning & Teaching in Action, Vol 2, Issue 1, 2003

    [9] Payne, A., Brinkman, W.-P. and Wilson, F., Towards Effective Feedback in e-Learning Packages: The Design of a Package to Support Literature Searching, Referencing and Avoiding Plagiarism, Proceedings of HCI2007 workshop: Design, use and experience of e-learning systems, 2007, pp. 71-75.

    [10] Dafoulas, G.A., The role of feedback in online learning communities, Fifth IEEE International Conference on Advanced Learning Technologies, 5-8 July 2005, pp. 827 - 831.

  • Similar Research Results (5)
  • Metrics
    views in OpenAIRE
    views in local repository
    downloads in local repository

    The information is available from the following content providers:

    From Number Of Views Number Of Downloads
    ROAR at University of East London - IRUS-UK 0 18
Share - Bookmark