Personalizing Access to Cultural Heritage Collections using Pathways

This paper discusses mechanisms for personalizing access to cultural heritage collections and suggests that paths or trails are a flexible and powerful model for this and could link with existing models of cognitive information behaviour. We also describe a European project called PATHS (Personalized Access To cultural Heritage Spaces) that aims to support information exploration and discovery through digital cultural heritage collections. This project aims to implement the user models discussed in this paper and provide a mechanism for users to create and share pathways through information spaces for learning and knowledge discovery. Personalized access to digital cultural heritage resources will be provided by adapting suggested routes to the requirements of individual users and groups, such as students/teachers, professional archivists and historians and scholars. Author

[1]  João Gama,et al.  An Adaptive Predictive Model for Student Modeling , 2006 .

[2]  Nigel Ford,et al.  Information retrieval and creativity: towards support for the original thinker , 1999, J. Documentation.

[3]  Owen Conlan,et al.  Metadata Driven Approaches to Facilitate Adaptivity in Personalized eLearning Systems , 2003 .

[4]  Jennifer Marlow,et al.  Extending Domain-Specific Resources to Enable Semantic Access to Cultural Heritage Data , 2009, J. Digit. Inf..

[5]  Federica Cena,et al.  Towards a Tag-Based User Model: How Can User Model Benefit from Tags? , 2007, User Modeling.

[6]  Daniel D. Garcia,et al.  Enhancing Digital Libraries with Social Navigation: The Case of Ensemble , 2010, ECDL.

[7]  C. A. Moore,et al.  Field-Dependent and Field-Independent Cognitive Styles and Their Educational Implications , 1977 .

[8]  Vincent Wade,et al.  Just-in-time Generation of Pedagogically Sound, Context Sensitive Personalized Learning Experiences , 2006 .

[9]  Vive Kumar,et al.  Assisting online helpers , 2005, Int. J. Learn. Technol..

[10]  Julio Gonzalo,et al.  Gathering requirements for multilingual search of audiovisual material in cultural heritage 1 , 2022 .

[11]  Jennifer Trant Tagging, Folksonomy and Art Museums: Early Experiments and Ongoing Research , 2009, J. Digit. Inf..

[12]  Peter Brusilovsky,et al.  Adaptive Hypermedia , 2001, User Modeling and User-Adapted Interaction.

[13]  Pattie Maes,et al.  Footprints: history-rich tools for information foraging , 1999, CHI '99.

[14]  Nigel Ford,et al.  Serendipity and information seeking: an empirical study , 2003, J. Documentation.

[15]  Platform Symphony,et al.  Smart recommendation for an evolving e-learning system: architecture and experiment. , 2007 .

[16]  A. Jameson Adaptive interfaces and agents , 2002 .

[17]  Christopher Brooks,et al.  Towards flexible learning object metadata , 2006 .

[18]  Lynn Silipigni Connaway,et al.  Mountains, Valleys, and Pathways: Serials Users' Needs and Steps to Meet Them , 2007 .

[19]  G. Pask STYLES AND STRATEGIES OF LEARNING , 1976 .

[20]  Jonathan P. Bowen,et al.  Personalization And The Web From A Museum Perspective , 2013 .

[21]  Julita Vassileva,et al.  Multi-Agent Multi-User Modeling in I-Help , 2003, User Modeling and User-Adapted Interaction.

[22]  Peter Brusilovsky,et al.  From adaptive hypermedia to the adaptive web , 2002, CACM.

[23]  Peter Dolog,et al.  The Personal Reader: Personalizing and Enriching Learning Resources Using Semantic Web Technologies , 2004, AH.

[24]  Guy J. Brown,et al.  Information systems and creativity: an empirical study , 2007, J. Documentation.

[25]  Themis Panayiotopoulos,et al.  A platform for virtual museums with personalized content , 2009, Multimedia Tools and Applications.

[26]  Les Carr,et al.  Where have you been from here? Trials in hypertext systems , 1999, CSUR.