MLTutor: An Application of Machine Learning Algorithms for an Adaptive Web-based Information System
13 (2): "Part II of the Special Issue on Adaptive and Intelligent Web-Based Systems "
One problem that commonly faces hypertext users, particularly in educational
situations, is the difficulty of identifying pages of information most relevant to their current
goals or interests. In this paper, we discuss the technical feasibility and the utility of applying
machine learning algorithms to generate personalised adaptation on the basis of a user's
browsing history in hypertext, without additional input from the user. In order to investigate the
viability of this approach, we developed a Web-based information system called MLTutor. The
design of MLTutor aims to remove the need for pre-defined user profiles and replace them with
a dynamic user profile-building scheme in order to provide individual adaptation. In MLTutor,
this adaptation is achieved by a combination of conceptual clustering and inductive machine
learning algorithms. An evaluation technique that probes the detailed effectiveness of the
adaptation is presented. The use of dynamic user profiles has been shown to be technically
feasible; however, while a superficial evaluation indicates that it is educationally effective, the
more thorough evaluation performed here shows that the positive results may be attributed to
other causes. This demonstrates the need for thorough evaluation of adaptive hypertext systems.