Volume Two - Academic Conferences

Cristina Wanzeller and Orlando Belo
several values of parameter settings. The learning approach was used, with success for all the WUM
processes from which was possible to obtain PMML documents, despite the need to complement the
description through explicit user interaction. We may say that the approach is effective.
Table 3: Description of the main extracted PMML items
PMML elements/attributes
(Header) Application version name KD Tool version and name
DataDictionary name, optype dataType field name, operation type (e.g., ordinal, continuous) and data
type
SequenceModel: functionName,
DM Function (e.g. sequences ) and Model (e.g. sequence)
algorithmName
SequenceModel: remain attributes parameter names and settings
MiningSchema fields used in the model and the played roles
5. Conclusions and future work
Web based eLearning platforms promoters and administrators, like other Web site sponsors, have
pressures to provide better services, using less resources and more efficiently. WUM may be a tool to
bridge the gap between the unknown within massive clickstream data and actionable knowledge, in
order to devise opportune enhancements. At least, site sponsors have to know how eLearning
platform is being used in general terms. However, clickstream data analysis and particularly WUM
learning curves are serious obstacles to inexperienced users, being pertinent to have a strategy
showing the way to proceed.
The proposed and developed work aims at promoting a more efficient, effective and synergetic
exploration of WUM potential. To achieve this aim we designed, developed and implemented a
prototype of a CBR system, specifically devoted to assist users on WUM processes, mainly on
selecting proper mining methods and approaches to address analysis problems. The system also
provides support to users on documenting and organizing the knowledge gained from the experience
on solving new WUM problems, through a semi-automatic learning approach.
We believe that the MPS system is a good tool for knowledge creation, sharing and reuse. The
general and specific tests conduct so far confirm the systems effectiveness. Currently, we have cases
with complexity substantially higher than the ones showed. However, one concern is to support cases
from the eLearning domain. We may say that the system is capable of dealing with experiences from
this area. The case representation approach is flexible and wide and, moreover, MPS is extensible.
For the future we plan to further evaluate the current implementation, using cases from the eLearning
domain. Furthermore, we intend to study the need to capture additional context information from the
eLearning sites. We want to study this specific area of application of WUM, in order to construct a
specialized case base. This specific WUM area, the problem types, the kinds of mining activities, the
related practical applications and the key data items are less studied and structured. Given so, our
first goal is to assure that the knowledge base can cope with specific domain, maintaining its
generality, but, at the same type, accommodating the rich details of these particular experiences.
References
Aamodt, A. and Plaza, E. (1994) “Case-Based Reasoning: Foundational Issues, Methodological Variations and
Systems Approaches”, Artificial Intelligence Communications, IOS Press, Vol7, pp.39-59.
Chorfi, H. and Jemni, M. (2004) “PERSO: Towards an Adaptive eLearning System”, Journal of Interactive
Learning Research, 15(4), pp. 433-447.
Herhskovitz, A. and Nachmias, R. (2009) “Learning about online learning processes and students’ motivation
through Web usage mining”, Interdisciplinary Journal of ELearning and Learning Objects, Vol. 5, pp.197-
214.
Kolodner, J. (1993) Case Based Reasoning, Morgan Kaufmann, San Francisco.
Lu, Jie (2004) “A Personalized eLearning Material Recommender System”, Proceedings of the 2nd International
Conference on Information Technology for Application. pp.374-379.
Mantaras, R., McSherry, D., Bridge, D., Leake, D., Smyth, B., Craw, S., Faltings, B., Maher, M.L., Cox, M.,
Forbus, K., Keane, M., Aamodt, A. and Watson, I. (2005) “Retrieval, Reuse, Revision, and Retention in
Case-Based Reasoning”, The Knowledge Engineering Review, Cambridge University Press DOI.
Pahl, C. (2004) “Data mining technology for the evaluation of learning content interaction”, International Journal
of ELearning, 3(4), 47-55.
Rafaeli, S., and Ravid, G. (1997) “Online, web based learning environment for an information systems course:
Access logs, linearity and performance”, Proc. of the Information Systems Education conference, USA.
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