N Natalia Stash

Creating and delivering adaptive courses with AHA

AHA! is an Open Source adaptive hypermedia platform, resulting from 10 years of experience with creating, using and improving on-line adaptive courses and presentations. This paper focuses on some recent additions to AHA! that are especially important for adaptive educational applications, namely stable presentations, adaptive link (icon) annotations and adaptive link destinations. We not only describe the technical aspects of these parts of AHA! but also illustrate their use in educational applications. We describe some fundamental limitations of Web-based adaptive applications, and show how AHA! deals with them in order to provide adaptation to prerequisite relationships in the way one would expect.

Recommendations based on semantically enriched museum collections

This article presents the CHIP demonstrator for providing personalized access to digital museum collections. It consists of three main components: Art Recommender, Tour Wizard, and Mobile Tour Guide. Based on the semantically enriched Rijksmuseum Amsterdam collection, we show how Semantic Web technologies can be deployed to (partially) solve three important challenges for recommender systems applied in an open Web context: (1) to deal with the complexity of various types of relationships for recommendation inferencing, where we take a content-based approach to recommend both artworks and art-history topics; (2) to cope with the typical user modeling problems, such as cold-start for first-time users, sparsity in terms of user ratings, and the efficiency of user feedback collection; and (3) to support the presentation of recommendations by combining different views like a historical timeline, museum map and faceted browser. Following a user-centered design cycle, we have performed two evaluations with users to test the effectiveness of the recommendation strategy and to compare the different ways for building an optimal user profile for efficient recommendations. The CHIP demonstrator received the Semantic Web Challenge Award (third prize) in 2007, Busan, Korea.

CHIP demonstrator: semantics-driven recommendations and museum tour generation

The main objective of the CHIP project is to demonstrate how Semantic Web technologies can be deployed to provide personalized access to digital museum collections. We illustrate our approach with the digital database ARIA of the Rijksmuseum Amsterdam. For the semantic enrichment of the Rijksmuseum ARIA database we collaborated with the CATCH STITCH project to produce mappings to Iconclass, and with the MultimediaN E-culture project to produce the RDF/OWL of the ARIA and Adlib databases. The main focus of CHIP is on exploring the potential of applying adaptation techniques to provide personalized experience for the museum visitors both on the Web site and in the museum.

Finding your way through the rijksmuseum with an adaptive mobile museum guide

This paper describes a real-time routing system that implements a mobile museum tour guide for providing personalized tours tailored to the user position inside the museum and interests. The core of this tour guide originates from the CHIP (Cultural Heritage Information Personalization) Web-based tools set for personalized access to the Rijksmuseum Amsterdam collection. In a number of previous papers we presented these tools for interactive discovery of users interests, semantic recommendations of artworks and art-related topics, and the (semi-)automatic generation of personalized museum tours. Typically, a museum visitor could wander around the museum and get attracted by artworks outside of the current tour he is following. To support a dynamic adaptation of the tour to the current user position and changing interests, we have extended the existing CHIP mobile tour guide with a routing mechanism based on the SWI-Prolog Space package. The package uses (1) the CHIP user profile containing users preferences and current location; (2) the semantically enriched Rijksmuseum collection and (3) the coordinates of the artworks and rooms in the museum. This is a joint work between the Dutch nationally funded CHIP and Poseidon projects and the prototype demonstrator can be found at http://www.chip-project.org/spacechip.

Interactive User Modeling for Personalized Access to Museum Collections: The Rijksmuseum Case Study

In this paper we present an approach for personalized access to museum collections. We use a RDF/OWL specification of the Rijksmuseum Amsterdam collections as a driver for an interactive dialog. The user gives his/her judgment on the artefacts, indicating likes or dislikes. The elicited user model is further used for generating recommendations of artefacts and topics. In this way we support exploration and discovery of information in museum collections. A user study provided insights in characteristics of our target user group, and showed how novice and expert users employ their background knowledge and implicit interest in order to elicit their art preference in the museum collections.

Adaptation languages as vehicles of explicit intelligence in adaptive hypermedia

This paper deals with a new challenge in Adaptive Hypermedia (AH) and web-based systems: finding the adaptation language to express, independently from the domain model or platform, the intelligent, adaptive behaviour of personalised web courseware. The major requirements for the ideal language are: reuse, flexibility, high level semantics, and ease of use. To draw closer to this ideal language, we compare two such language proposals: LAG, a generic adaptation language, and a new XML adaptation language for Learning Styles (LS) in AHA!, LAG-XLS.

AHA! Meets AHAM

After many years of hypertext research, the Dexter model was defined [7] to capture the features of most existing hypertext systems in a single, formal reference model. Likewise, the AHAM model [5] (based on Dexter) describes most features that are found in adaptive hypermedia systems (ahs). In the AHA! project funded by the NLnet Foundation we are extending the simple AHA system [4, 6] with the condition-action rules that were introduced in AHAM [8]. This results in a more versatile adaptation engine, with more intuitive and more powerful rules.

Authoring and Management Tools for Adaptive Educational Hypermedia Systems: The AHA! Case Study

Creating and maintaining adaptive educational applications is hard work for teachers and developers. In order to help the author perform these tasks the e-learning systems must provide authoring and management tools. In this chapter we describe several useful tools for working with adaptive educational hypermedia systems, using the Adaptive Hypermedia Architecture (AHA!) system. AHA! is a well-known open source general-purpose adaptive hypermedia system. In the current AHA! distribution versions there are some general adaptive author tools as Concept Editor, Graph Editor, and Form Editor, all accessible through the overall Application Management Tool. There is also a specific educational tool: the Test Editor (and the associated Test Engine) and we are now developing some others such as a Course Editor and Mining tool. In this chapter we describe the AHA! system and the functionality of each of these authoring and management tools intended to help teachers and application developers.

AHA! the next generation

AHA! is a simple Web-based adaptation engine that was originally developed to support an on-line course. This paper describes AHA! version 2.0, a new major release that aims to significantly increase the adaptive versatility of AHA! without sacrificing AHA!s simplicity that makes it easy to use. The new features in AHA! are inspired by AHAM [4], a Dexter [6] based reference model for adaptive hypermedia systems.

Learning styles adaptation language for adaptive hypermedia

Typically, the behavior of adaptive systems is specified by a set of rules that are hidden somewhere in the system’s implementation. These rules deal with instances of the domain model. Our purpose is to specify the adaptive response of the system at a higher level (to be applied and reused for different domains or adaptive applications) in an explicit form, called adaptation language. For this purpose we have chosen learning styles (LS) as an implementation field. We defined an XML-based adaptation language LAG-XLS for the AHA! system. In this paper we focus on the empirical evaluation of LAG-XLS.