dc.contributor.author | Oboko, Robert O | |
dc.contributor.author | Wagacha, Peter W | |
dc.contributor.author | Masinde, Euphraith M | |
dc.contributor.author | Omwenga, Elijah | |
dc.contributor.author | Libotton, Arno | |
dc.date.accessioned | 2013-07-02T15:19:00Z | |
dc.date.available | 2013-07-02T15:19:00Z | |
dc.date.issued | 2008 | |
dc.identifier.citation | Robert O. Oboko, Peter W. Wagacha, Euphraith M. Masinde et al (2008). Value Difference Metric for Student Knowledge Level initialization in a Learner Model-based Adaptive e-Learning System. Strengthening the Role of ICT in Development, Vol. IV | en |
dc.identifier.uri | http://hdl.handle.net/11295/44284 | |
dc.description.abstract | Web-based learning systems give students the freedom to determine what to study
based on each individual learner’s learning goals. These systems support learners in
constructing their own knowledge for solving problems at hand. However, in the
absence of instructors, learners often need to be supported as they learn in ways that
are tailored to suit a specific learner. Adaptive web-based learning systems fit in such
situations. In order for an adaptive learning system to be able to provide learning
support, it needs to build a model of each individual learner and then to use the
attribute values for each learner as stored in the model to determining the kind of
learning support that is suitable for each learner. Examples of such attributes are
learner knowledge level, learning styles and learner errors committed by learners
during learning. There are two important issues about the use of learner models.
Firstly, how to initialize the attributes in the learner models and secondly, how to
update the attribute values of the learner model as learners interact with the learning
system. With regard to initialization of learner models, one of the approaches used
is to input into a machine learning algorithm attribute values of learners who are
already using the system and who are similar (hence called neighbors) to the learner
whose model is being initialized. The algorithm will use these values to predict initial
values for the attributes of a new learner. Similarity among learners is often expressed
as the distance from one learner to another. This distance is often determined using
a heterogeneous function of Euclidean and Overlap measures (HOEM). This paper
reports the results of an investigation on how HOEM compares to two different
variations of Value Difference Metric (VDM) combined with the Euclidean measure
(HVDM) using different numbers of neighbors. An adaptive web-based learning
system teaching object oriented programming was used. HOEM was found to be
more accurate than the two variations of HVDM. | en |
dc.language.iso | en | en |
dc.title | Value Difference Metric for Student Knowledge Level initialization in a Learner Model-based Adaptive e-Learning System | en |
dc.type | Book chapter | en |