Personalized Learning Paths Based on Wikipedia Article Statistics

Abstract

Abstract:We propose a new semi-automated method for generating personalized learning paths from the Wikipedia online encyclopedia by following inter-article hyperlink chains based on various rankings that are retrieved from the statistics of the articles. Alternative perspectives for learning topics are achieved when the next hyperlink to access is selected based on hierarchy of hyperlinks, repetition of hyperlink terms, article size, viewing rate, editing rate, or user-defined weighted mixture of them all. We have implemented the method in a prototype enabling the learner to build independently concept maps following her needs and consideration. A list of related concepts is shown in a desired type of ranking to label new nodes (titles of target articles for current hyperlinks) accompanied with parsed explanation phrases from the sentences surrounding each hyperlink to label directed arcs connecting nodes. In experiments the alternative ranking schemes well supported various learning needs suggesting new pedagogical networking practices. INTRODUCTIONTo enhance the quality and efficiency of automated information processing there is a need for new computational methods. The methods used for representing and modifying information with the computers should be made more compatible with the methods that are naturally used by humans to adopt and associate meanings. Thus developing illustrative adaptive computational methods that can be used in knowledge management in natural language and with intuitive visualizations should have a high priority in research agenda. We suggest that information available in structured online knowledge bases can serve as a valuable resource for computer-assisted learning in respect to the key concepts of curriculum and semantic relations linking them. We propose a new adaptive method to assist individual learning of core network of semantic relations in curriculum and later expanding this network further. We consider that still today the most valuable environment for learning is social collaboration in everyday life. We do not aim to challenge this traditional method but to complement it in a fruitful manner. We propose a new semi-automated method for generating personalized learning paths from the Wikipedia online encyclopedia by following inter-article hyperlink chains based on various rankings that are retrieved from the statistics of the articles. A learning path describes a structure of actions a learner has to perform in order to attain a competence or a competence profile (Janssen et al., 2008). In our proposal the learning paths are represented with concept maps. We are interested in methodology related to semantic navigation, intelligent tutoring systems and content-based filtering.Our method aims to ensure that an optimal coverage of concepts becomes provided to the learner. Especially our method aims to enable active explorations of conceptual relations taking into account various diverse perspectives that are based on different individual backgrounds and preference. Besides in...