Now showing items 2-4 of 4

  • Harnessing background knowledge for e-learning recommendation. 

    Mbipom, Blessing; Craw, Susan; Massie, Stewart (Springer https://dx.doi.org/10.1007/978-3-319-47175-4_1, 2016-11-05)
    MBIPOM, B., CRAW, S. and MASSIE, S. 2016. Harnessing background knowledge for e-learning recommendation. In Bramer, M. and Petridis, M. (eds.) 2016. Research and development in intelligent systems XXXIII: incorporating applications and innovations in intelligent systems XXIV. Cham: Springer [online], pages 3-17. Available from: https://dx.doi.org/10.1007/978-3-319-47175-4_1
    The growing availability of good quality, learning-focused content on the Web makes it an excellent source of resources for e-learning systems. However, learners can find it hard to retrieve material well-aligned with ...
  • Knowledge modelling for a generic refinement framework 

    Boswell, Robin; Craw, Susan (Elsevier http://dx.doi.org/10.1016/S0950-7051(99)00018-0, 1999-10)
    BOSWELL, R. and CRAW, S., 1999. Knowledge modelling for a generic refinement framework. Knowledge Based Systems, 12 (5-6), pp. 317-325
    Refinement tools assist with debugging the knowledge-based system (KBS), thus easing the well-known knowledge acquisition bottleneck, and the more recently recognised maintenance overhead. The existing refinement tools ...
  • Learning adaptation knowledge to improve case-based reasoning 

    Craw, Susan; Wiratunga, Nirmalie; Rowe, Ray (Elsevier http://dx.doi.org/10.1016/j.artint.2006.09.001, 2006-11)
    CRAW, S., WIRATUNGA, N. and ROWE, R., 2006. Learning adaptation knowledge to improve case-based reasoning. Artificial Intelligence, 170 (16-17), pp. 1175-1192.
    Case-Based Reasoning systems retrieve and reuse solutions for previously solved problems that have been encountered and remembered as cases. In some domains, particularly where the problem solving is a classification task, ...