“KWARC was!”

Presentation by Mirjam Köck at Lernen, Wissen, Adaptivität (LWA 2008), University of Würzburg, 6.-8. October 2008. Track: ABIS

Motivation: eLearning has become very popular, but still simple approaches of implementing adaptivity dominate. Computational intelligence is under-represented. The authors focus on challenges such as the autonomous knowledge acquisition, autonomous pattern identification at run-time, and expression of patterns in rules.

Adaptive learning guidance includes

• navigation through learning materials
• Guiding communication and cooperation activities (suggestion communication partners, contact persons for questions, …)
• …

Approach: Using communication and collaboration activities (rather than contents/ tags) are used as input for the user models. Identify groups of learners based on communication and learning behaviours (observing the style of learning, the level of activities, activity pattern).

Collecting information such as the user’s online time, actions related to communication (read, write, update, delete), user’s current knowledge, learning activities (time needed for test, time spent on content before taking tests, performance of tests), content of communication items

Relations of interest: How is a user’s time spent on communication related to the learning curriculum?; does the knowledge state influence communication?; what is the degree of similarity between a user’s activity level in the communication area and content area?; …

Promising Technologies: Artificial Neural Networks (can discover activity clusters, can adapt components e.g. change weights, do not depend on continuous human intervention; but: blackbox-syndrome, missing explanation capability, rule extraction is difficult); Combined Neuro-Fuzzy Approaches, Bayesian Networks (combination of domain knowledge and data; derivation of causal relationships) -> Combination allows using Neural Networks e.g. for learning and making the hidden sector of Neural Networks more visible.

Prospect: Improve adaptation; reducing human efforts to ensure quality and up-to-dateness of model data; semi-automatic pattern recognition, classification and evaluation at run-time; predication of behaviour based on correlations; integration of CI approaches into popular learning environment (Sakai; see also Stephan Weibelzahl)

Presentation by Thomas Fischer (University of Jena) at Lernen, Wissen, Adaptivität (LWA 2008), University of Würzburg, 6.-8. October 2008. Track: ABIS.

Mashups extract and combine data, functionalities, etc. from different websites into a single integrated tool.

See also Why Mashups = (REST + ‘Traditional SOA’) * Web 2.0

Presentation by Stephan Weibelzahl (National College of Irland) at Lernen, Wissen, Adaptivität (LWA 2008), University of Würzburg, 6.-8. October 2008. Track: ABIS.

Stephan Weibelzahl developed HTML-Tutor, an interactive learning environment which offers an introduction to HTML and publishing on the Web.

Most system in academia remain at the prototype level with poor usability. This is acceptable as prototypes are vehicles to provide proof of concepts of an approach. However, in the long run scientists need to demonstrate the effects and impact of adaptive systems. Consequently, we need to start taking usability criteria into account when developing and evaluating scientific software. The authors aim at analysing the effects of usability on adaptation.

An adaptive peer finder is presented. The system is based on the adaptive learning system AHA! by de Bra and Calvi.

Further Readings:

• AHA! website: Adaptive Hypermedia for All
• The Design of AHA!

Presentation by Sirko Schindler (University of Jena) at Lernen, Wissen, Adaptivität (LWA 2008), University of Würzburg, 6.-8. October 2008. Track: ABIS (collaboration with Andreas Nauerz). (see paper)

Treemaps were proposed by Johnson & Schneiderman in 1991. The author propose adaptive treemaps – displaying different treemaps to different users – to improve the navigation in web portals. To test several algorithms, the author developed a prototype, which is embedded into the IBM WebSphere Portal.

Presentation by Arne Koesling at Lernen, Wissen, Adaptivität (LWA 2008), University of Würzburg, 6.-8. October 2008. Track: ABIS

Focus: Introduce Adaptability into regular Learning Management Systems (LMS) – introduce sophisticated rule systems (policies) that allow all stakeholders (learners , teachers, system admins) to adapt the system for their needs.

Policies are statements that define the behaviour of the system and are intended to guide decisions and actions. There are also known as business rules. For example: “New customers have to pay in advance, regular customers are allowed to pay after delivery” or “point all users to the basic material, if they enter the course the first time and notify all tutors”. Policies are dynamic, declarative, reusable, have a well-defined semantics, and allow for reasoning.

The author chose Protune as policy language, which additionally supports negotiations, explanations and integration with external sources (see concrete examples in Protune on the w3.org website).

Presentation by Andreas Nauerz (IBM, University of Jena) at Lernen, Wissen, Adaptivität (LWA 2008), University of Würzburg, 6.-8. October 2008. Track: ABIS (see paper)

Focus: Improving accessibility of web portals. Company web portals (Enterprise Information Portals) often include immense corpora of contents, which are hardly ever used by the user as selection and navigation is often too tedious. Main concepts of the authors have been integrated in the IBM WebSphere Portal.

The author propose a complex user and context model (date, time, location). The user model reflect the user’s interest and preference: Information from static profiles (native language, home country, working location, age, …), the user’s interaction behaviour (pages and portlets they work with, tags users apply to resources); and the user’s social networks are used to derive knowledge on the user’s needs. For example, the user model includes information on the tagging, rating, and commenting behaviour of users: Tagging and rating behaviour are analysed to understand interest and preference of single users and entire communities. The static data is entered by the user, while more dynamic data is extracted from the user interaction using web usage mining.

Based on the user models two main services are provided:

• Content adaptation: navigation and page layouts (improve accessibility of contents that users frequently use; make more content accessible but adapting its structure/ layout and make its relevance obvious to the user)
• Content recommendation: based on background information, related content, or activities of experts and similar behaving users (make recommendation of new material, which relevance is not obvious to the user)

Three independent context profile are created: (1) travelling, (2) office, (3) at home: User activities during are only stored in the respective context. For example, activity during travelling do not influence the user modelling for office work or at home.

Tags can be associate to web pages, documents, fragments of pages (very granular). They can be typed by the users or their semantics can be automatically extracted by calling respective back-end services.

Keynote Alexandros Paramythis Lernen, Wissen, Adaptivität (LWA 2008), University of Würzburg, 6.-8. October 2008. Track: ABIS (see slides)

Current focus of Alexandros Paramythis:

• Adaptation on the basis of collaborative user activities
• Meta-Adaptivity
• Evaluation of Adaptivity (see also Stephan Weibelzahl)

Focus of the talk: Is the research community on user modelling and adaptivity still relevant and (if so) in what ways?

Netflix Prize: The competition on improving Netflix’s accuracy of the prediction by 10% gives as insights in the maturity of the community. It was not possible to make the improvement of 10% immediately, indicating some level of maturity. But still the community is not yet mature enough as a 10% improvement is still consider realistic (in contrast to more mature communities such as the database researchers).

Web 2.0 paradigms: Bringing user participation and contribution to Web 2.0 content. The emphasis is on social behaviours/ online ways to connect with people with similar interest. Two aspects: (1) creating social graphs (networks) summarizing relation between people and (2) creating tag clouds to summarize relations between concepts (expressing people’s interest via their tags). Both structures can get unwieldy – so the challenge is to filter and adapt these structures. Researchers are starting on personalization of tag clouds and social networks now.

Are adaptive hypermedia different to adaptive systems? Is this really an important question to ask? Or should we no longer distinguish these two fields?

Some under-explored adaptivity topics are: (1) social dimension of users; (2) collaboration/ cooperation; and (3) user activities.

How does user profile change in Web2.0? Are we (in academia) aware of approaches in industry? There is almost no cooperation between academia and industry although the latter came up with several de facto standards:

• DataPortability initiative: “Data Portability is the option to use your personal data between trusted applications and vendors.”
• OpenSocial (promoted by Google): “OpenSocial defines a common API for social applications across multiple websites. With standard JavaScript and HTML, developers can create apps that access a social network’s friends and update feeds.”
• OpenID: “OpenID is a shared identity service, which allows Internet users to log on to many different web sites using a single digital identity, single sign-on, eliminating the need for a different user name and password for each site.”
• APML – Attention Profiling Mark-up Language: “APML allows users to share their own personal Attention Profile in much the same way that OPML allows the exchange of reading lists between News Readers.”

Adaptivity in Ubiquity: We have to address new adaptive scenarios. Also here industry has contributed already: see e.g. IBMS emotion mouse; see Geotagging

Ubiquity in adaptive eLearning: Computer have turned into tutors almost as good as human instructors. But mist learning takes place outside the classroom. We can not easily observe these learning processes; adaptive system do not support these informal and hidden learning processes.

Security Concerns: Some adaptation algorithms are to weak (see Amazon … case). We can strengthen the algorithms and/or increase the investment required for significant effects to the system’s adaptive behaviour.

Quality: Adaptation quality is an illusive goal as it is hard to define it (harder than e.g. interaction quality). Moreover, the success of adaptation often depends on the application domain and the overall experience of the user. Thus, evaluation adaptation is difficult (see Stephan Weibelzahl). We need to isolate adaptation from the other components to measure its impact without the influence of other aspects of the system (e.g. the user model or user interface).

The adaptation and user modelling community is a rather closed community. Access to publication is limited due to copy rights. Moreover, researchers tend to reinvent the wheel over and over again. Industry is completely ignored although they already widely use their de facto standards like OpenID … We should become aware of this and start to considering existing industrial approaches as well as start collaborations between industry and academia.