Educational tool based on topology and evolution of hyperlinks in the Wikipedia

We propose a new method to support educationalexploration in the hyperlink network of the Wikipedia onlineencyclopedia. The learner is provided with alternative parallelranking lists, each one promoting hyperlinks that represent adifferent pedagogical perspective to the desired learning topic.The learner can browse the conceptual relations between thelatest versions of articles or the conceptual relations belongingto consecutive temporal versions of an article, or a mixture ofboth approaches. Based on her needs and intuition, the learnerexplores hyperlink network and meanwhile the method buildsautomatically concept maps that reflect her conceptualizationprocess and can be used for varied educational purposes.Initial experiments with a prototype tool based on the methodindicate enhancement to ordinary learning results and suggestfurther research.Peer reviewe

Educational tool based on topology and evolution of hyperlinks in the Wikipedia

Abstract-We propose a new method to support educational exploration in the hyperlink network of the Wikipedia online encyclopedia. The learner is provided with alternative parallel ranking lists, each one promoting hyperlinks that represent a different pedagogical perspective to the desired learning topic. The learner can browse the conceptual relations between the latest versions of articles or the conceptual relations belonging to consecutive temporal versions of an article, or a mixture of both approaches. Based on her needs and intuition, the learner explores hyperlink network and meanwhile the method builds automatically concept maps that reflect her conceptualization process and can be used for varied educational purposes. Initial experiments with a prototype tool based on the method indicate enhancement to ordinary learning results and suggest further research

Characterising Web Site Link Structure

The topological structures of the Internet and the Web have received considerable attention. However, there has been little research on the topological properties of individual web sites. In this paper, we consider whether web sites (as opposed to the entire Web) exhibit structural similarities. To do so, we exhaustively crawled 18 web sites as diverse as governmental departments, commercial companies and university departments in different countries. These web sites consisted of as little as a few thousand pages to millions of pages. Statistical analysis of these 18 sites revealed that the internal link structure of the web sites are significantly different when measured with first and second-order topological properties, i.e. properties based on the connectivity of an individual or a pairs of nodes. However, examination of a third-order topological property that consider the connectivity between three nodes that form a triangle, revealed a strong correspondence across web sites, suggestive of an invariant. Comparison with the Web, the AS Internet, and a citation network, showed that this third-order property is not shared across other types of networks. Nor is the property exhibited in generative network models such as that of Barabasi and Albert.Comment: To appear at IEEE/WSE0

Educational concept mapping method based on high-frequency words and Wikipedia linkage

We propose a computational method to support the learner's knowledge adoption based on conceptmapping relying on three perspectives of learning scenario represented by learning concept networks:learner’s knowledge, learning context and learning objective. Each learning concept network isgenerated based on a set of high-frequency words from a representative text sample that are connectedbased on the shortest hyperlink chains between corresponding Wikipedia articles. The learner exploresranking-based routings connecting learning concept networks by expanding a concept map in twocomplementing learning modes: assisted construction and assistive evaluation, with focused andcontextualized emphasis. Based on the method we have implemented a prototype of an educational tooland its preliminary testing indicated that the method can well support personalized knowledge adoption.Peer reviewe

ConceptMapWiki - a collaborative framework for agglomerating pedagogical knowledge

We propose a new educational framework,ConceptMapWiki, that is a wiki representing pedagogicalknowledge with a collection of concept maps which iscollaboratively created, edited and browsed. The learners andeducators provide complementing contribution to evolvingshared knowledge structures that are stored in a relationaldatabase forming together inter-connected overlappingontologies. Every contribution is stored supplied with timestamps and a user profile enabling to analyze maturing ofknowledge according to various learner-driven criteria.Pedagogically motivated learning paths can be collaborativelydefined and evaluated, and educational games can beincorporated based on browsing and editing concept maps.The proposed framework is believed to be the first wikiarchitecture of it's kind, designed for personalized learningwith an evolving knowledge repository relying on adaptivevisual representations and sound pedagogical motivation.Initial experiments with a functional online prototype indicatepromising educational gain and suggest further research.Peer reviewe

Human exploration of complex knowledge spaces

Driven by need or curiosity, as humans we constantly act as information seekers. Whenever we work, study, play, we naturally look for information in spaces where pieces of our knowledge and culture are linked through semantic and logic relations. Nowadays, far from being just an abstraction, these information spaces are complex structures widespread and easily accessible via techno-systems: from the whole World Wide Web to the paramount example of Wikipedia. They are all information networks. How we move on these networks and how our learning experience could be made more efficient while exploring them are the key questions investigated in the present thesis. To this end concepts, tools and models from graph theory and complex systems analysis are borrowed to combine empirical observations of real behaviours of users in knowledge spaces with some theoretical findings of cognitive science research. It is investigated how the knowledge space structure can affect its own exploration in learning-type tasks, and how users do typically explore the information networks, when looking for information or following some learning paths. The research approach followed is exploratory and moves along three main lines of research. Enlarging a previous work in algorithmic education, the first contribution focuses on the topological properties of the information network and how they affect the \emph{efficiency} of a simulated learning exploration. To this end a general class of algorithms is introduced that, standing on well-established findings on educational scheduling, captures some of the behaviours of an individual moving in a knowledge space while learning. In exploring this space, learners move along connections, periodically revisiting some concepts, and sometimes jumping on very distant ones. To investigate the effect of networked information structures on the dynamics, both synthetic and real-world graphs are considered, such as subsections of Wikipedia and word-association graphs. The existence is revealed of optimal topological structures for the defined learning dynamics. They feature small-world and scale-free properties with a balance between the number of hubs and of the least connected items. Surprisingly the real-world networks analysed turn out to be close to optimality. To uncover the role of semantic content of the bit of information to be learned in a information-seeking tasks, empirical data on user traffic logs in the Wikipedia system are then considered. From these, and by means of first-order Markov chain models, some users paths over the encyclopaedia can be simulated and treated as proxies for the real paths. They are then analysed in an abstract semantic level, by mapping the individual pages into points of a semantic reduced space. Recurrent patterns along the walks emerge, even more evident when contrasted with paths originated in information-seeking goal oriented games, thus providing some hints about the unconstrained navigation of users while seeking for information. Still, different systems need to be considered to evaluate longer and more constrained and structured learning dynamics. This is the focus of the third line of investigation, in which learning paths are extracted from advances scientific textbooks and treated as they were walks suggested by their authors throughout an underlying knowledge space. Strategies to extract the paths from the textbooks are proposed, and some preliminary results are discussed on their statistical properties. Moreover, by taking advantages of the Wikipedia information network, the Kauffman theory of adjacent possible is formalized in a learning context, thus introducing the adjacent learnable to refer to the part of the knowledge space explorable by the reader as she learns new concepts by following the suggested learning path. Along this perspective, the paths are analysed as particular realizations of the knowledge space explorations, thus allowing to quantitatively contrast different approaches to education

Ensimmäinen ja toinen käsikirjoitusversio väitöskirjaa varten

This publication contains the first and the second manuscript version for LauriLahti’s doctoral dissertation in 2015 "Computer-assisted learning based on cumulative vocabularies, conceptual networks and Wikipedia linkage".Tämä julkaisu sisältää ensimmäisen ja toisen käsikirjoitusversion Lauri Lahden väitöskirjaan vuonna 2015 "Tietokoneavusteinen oppiminen perustuen karttuviin sanastoihin, käsiteverkostoihin ja Wikipedian linkitykseen".Not reviewe

Scale-free law: network science and copyright

No description supplie

Täydennysosa väitöskirjaan "Tietokoneavusteinen oppiminen perustuen karttuviin sanastoihin, käsiteverkostoihin ja Wikipedian linkitykseen"

A supplement to Lauri Lahti’s doctoral dissertation in 2015 "Computer-Assisted Learning Based on Cumulative Vocabularies, Conceptual Networks and Wikipedia Linkage" so that this supplement was referenced to by the original publication.Täydennysosa väitöskirjaan "Tietokoneavusteinen oppiminen perustuen karttuviin sanastoihin, käsiteverkostoihin ja Wikipedian linkitykseen"Not reviewe

A framework for structuring prerequisite relations between concepts in educational textbooks

In our age we are experiencing an increasing availability of digital educational resources and self-regulated learning. In this scenario, the development of automatic strategies for organizing the knowledge embodied in educational resources has a tremendous potential for building personalized learning paths and applications such as intelligent textbooks and recommender systems of learning materials. To this aim, a straightforward approach consists in enriching the educational materials with a concept graph, i.a. a knowledge structure where key concepts of the subject matter are represented as nodes and prerequisite dependencies among such concepts are also explicitly represented. This thesis focuses therefore on prerequisite relations in textbooks and it has two main research goals. The first goal is to define a methodology for systematically annotating prerequisite relations in textbooks, which is functional for analysing the prerequisite phenomenon and for evaluating and training automatic methods of extraction. The second goal concerns the automatic extraction of prerequisite relations from textbooks. These two research goals will guide towards the design of PRET, i.e. a comprehensive framework for supporting researchers involved in this research issue. The framework described in the present thesis allows indeed researchers to conduct the following tasks: 1) manual annotation of educational texts, in order to create datasets to be used for machine learning algorithms or for evaluation as gold standards; 2) annotation analysis, for investigating inter-annotator agreement, graph metrics and in-context linguistic features; 3) data visualization, for visually exploring datasets and gaining insights of the problem that may lead to improve algorithms; 4) automatic extraction of prerequisite relations. As for the automatic extraction, we developed a method that is based on burst analysis of concepts in the textbook and we used the gold dataset with PR annotation for its evaluation, comparing the method with other metrics for PR extraction