1. Adaptive Hypermedia 2ID20
Prof. dr. Paul De Bra
Welcome to week 4 of the course on Adaptive Hypermedia.
In this part we will give an overview of existing hypermedia systems and applications and also show some evaluations.

2. Example Adaptive Hypermedia Systems
We show examples that are very different:
AIMS: Adaptive Information Management System (TU/e+UT)
ISIS-Tutor: Tutorial for Library Information System (Moscow State University and Univ. of Trier)
SQL-Tutor: Intelligent Tutoring System for SQL (Canterbury, New Zealand)
Interbook: Adaptive Electronic Textbooks (Univ. of Pittsburgh)
INTRIGUE: adaptable tourist guide (Univ. of Torino)
TV Scout: personalized TV guide (GMD Darmstadt)
HERA: Data Integration and Presentation Generation in Web-Based Information Systems (TU/e)
The ARIA Photo Agent (MIT) with commonsense reasoning
In this part of the adaptive hypermedia course we are going to look at examples that are very different.
We start with AIMS, an adaptive task-based browsing and retrieval system for educational applications, developed at the University of Twente.
We will look at ISIS-Tutor, SQL-Tutor and Interbook, three systems that show a clear evolution in the history of adaptive educational systems.
We then try the INTRIGUE system that offers potential tourists to the Torino area some advice as to which places to visit.
TV Scout is an example of a personalized TV guide. It can give you advice on what to watch on television tonight or later this week, based on your preferences and on feedback from other users.
We briefly demonstrate HERA, the data integration and presentation generation research performed in our university.
Finally we show the ARIA Photo Agent from MIT, a system that lets you use and annotate photos with very little effort.

3. AIMS: Task-Based Information Retrieval
Agent-based Information Management System:
concept visualization (using “aquabrowser”)
task-based search (keyword search extended with task information)
user model: keeps track of user’s knowledge and performed tasks
graphical user-interfaces for creating concepts, tasks, courses, etc.
evaluated with students from Universiteit Twente
AIMS, or Agent-based Information Management System, is an educational hypermedia and information retrieval system for task-based learning.
It offers students different ways to access and learn the topics of a course:
It has a graphical, graph-based browser for concepts, called “aquabrowser”. The interface shows a fish-eye view of the concept structure. The view is updated as you browse through the concept structure.
In AIMS the student is working on a task that belongs to a course. (There is also a no-task mode.) The second way to use AIMS is to perform a search for keywords. The search is augmented with the task-related keywords to give results that are not only relevant for the search terms but also for the current task.
AIMS stores the user’s knowledge and information about visited documents and performed tasks in a user model.
AIMS has been used and evaluated with students from the educational technology department at the University of Twente.
We describe the user interface for students and for instructors in detail.

4. AIMS Global Information Model
Domain model: defines subject domain by means of a concept map
concepts are linked to each other (“ontology”)
Library model: defines relationship between documents and concepts
how relevant is a document for a given concept
Course model: course topics and tasks
tasks are described using concepts, task description, prerequisites, task status
Learner model: what the user has learned:
course tasks, domain concepts, library documents
overlay model
built jointly by the user and the system
We first have a look at the overall information architecture of AIMS.
The subject domain of an application or course is described by means of a domain model. This is done through a concept map (CM). In the concept map the concepts are linked to each other. The links are qualified to indicate the type of the relationship. The domain model thus describes an ontology.
In the library model documents are linked to concepts. The library model can describe this independent of the course or courses in which the documents and concepts are used.
The course model describes a course in terms of tasks the user has to perform. Tasks teach the student about concepts. There are also prerequisites to indicate that certain things must be done before certain other things.
The learner model is an overlay model indicating the status of the user for tasks, the knowledge about concepts and the documents that were read. The user has influence on the model and can indicate that certain concepts are already known.

5. AIMS Student Interface
This viewgraph shows the user-interface for students. The student can first of all use the menus to select a task to work on and to indicate which concepts related to the task are already known.
The interaction can then be done through the topic map or the search form.
Normally the student will perform a search to find documents about a topic related to the task. The system then performs the following functions:
The search is augmented with terms related to the task, in order to find documents that are relevant for both the search terms given by the student and the concepts that relate to the task.
The graphical output shows the concepts that are related to the search result. The result list on the right shows the pages that were found. In a newer version of the interface the list is annotated to indicate how relevant the documents are to the given task and also to indicate whether the document was read before.
When the user moves the mouse over a concept in the concept map the system highlights the search results that correspond to this concept.
When the user moves the mouse over a search result the system highlights the concepts in the map that correspond to this search result.

6. AIMS Instructor Domain Environment
The AIMS Domain Editor lets the instructor externalize his/her knowledge about a specific subject domain through a concept map. The map links all the domain concepts or terms. Links have types and weights to indicate how concepts relate to each other and how strong the links are.
The domain editor also lets you enter relationships between concepts and documents. Such relationships do not really belong to the domain model (but to the library model). However, it is often practical to link concepts and documents while creating the domain model for a course.

7. AIMS Instructor Library Environment
The AIMS Library Editor lets the instructor associate local and remote documents to concepts. The keywords, optionally with weights, are important to allow the search function to associate documents with the user’s search terms.
The assocations between concepts and documents are independent of the domain model for a course. When a document is relevant for learning a concept it is relevant for this concept in all courses.

8. AIMS Instructor Course Environment
The AIMS Course Editor lets the instructor define course topics and tasks. Each task is tied to concepts that need to be learned. Through these topics and the links between concepts and documents the system can infer which documents are relevant for the task.

9. AIMS Admin Environment
AIMS also has an administration interface for creating courses and administrating users. The administrator can create courses and define users and user groups.
Students in AIMS cannot simply register themselves but must be allowed to enter by the administrator.

10. ISIS-Tutor: adaptive annotation/hiding
Tutor for CDS/ISIS library system
CDS/ISIS is a library system for PCs sponsored by UNESCO
ISIS Tutor developed by Peter Brusilovsky and Leonid Pesin
descendent from an older system ITEM/P (Moscow State Univ.)
domain- and student model for monitoring student knowledge
tutor component to perform adaptive task sequencing
hypertext component lets students navigate through course material.
learning environment lets users interact with ISIS
versions with adaptive link annotation and link removal
evaluated to determine learning effect of using adaptation
ISIS-Tutor is an intelligent tutor for the CDS/ISIS library system that was developed by UNESCO.
The ISIS-Tutor was developed at the International Centre for Scientific and Technical Information (ICSTI) and Moscow State University, by Peter Brusilovsky and Leonid Pesin.
The interrelated domain model and student model form the heart of the system. It makes the system integrated and adaptive. Modules of ISIS-Tutor use the student model to adapt their work and update it to reflect the student's progress.
The tutor component supports adaptive task sequencing, which means that knowledge demonstrated by the student in the past is analyzed and the system selects an optimal teaching operation to perform. The component deals with three kinds of teaching operations: concept presentations, examples and problems. Using the Student Model and the available tasks the tutor can select an optimal teaching operation for the given student in each stage of the learning process.
The hypertext component supports student-driven acquisition of conceptual knowledge. It is an integrated part of the system. It means that the component uses the student model to provide adaptive navigation support for the given student, and it updates the student model to reflect the results of the student's work with the component.
The learning environment allows the user to play and experiment with print formatting commands. It provides step-by-step execution and extended visualization. Student work in this component is also reflected in the student model.
Three different version of ISIS Tutor were tried in evaluation experiments: one without adaptive navigation support, one with adaptive link annotation and one with annotation and the removal of inappropriate links. We show the versions and the evaluation on separate viewgraphs.

11. ISIS Tutor with Link Annotation
This screenshot shows the main menu for ISIS Tutor with adaptive link annotation.
The color scheme is that the red links are items that are ready to be studied. The green items have been studied and are considered known. The gray-ish items are not appropriate for the user at this time.

12. ISIS Tutor with Link Removal+Annotation
This screenshot shows ISIS Tutor again, but this time with link removal as well as annotation. All the red items are ready to be studied, whereas the green ones are already learned. The inappropriate items are simply not available in this version.

13. Evaluation of ISIS Tutor (number of steps)
ISIS Tutor was evaluated in three versions: one without adaptivity, one with adaptive link annotation, and one with adaptive link hiding and annotation. The difference between the non-adaptive and the adaptive versions is very clear. The version with link hiding required fewer navigation steps, but that difference is not really significant, especially because in the version with just annotation not all the menu items fit on the screen, so additional steps were needed to jump from screen 1 to screen 2 of the menu.

14. Evaluation of ISIS Tutor (repetitions)
This diagram shows that the adaptive annotation and removal techniques result in a significant reduction of the number of repeat visits to pages. This is not surprising: when users get no advice they go to pages that are not yet appropriate. They go back and continue learning and later revisit the page to study it when appropriate.
The adaptive annotations give guidance that is clearly followed by the students. They study pages at the right time and thus have no need to revisit them later.

15. Relationship between well-known AHS
ITEM/IP, MSU ( )
ISIS-Tutor, MSU ( )
ITEM/PG, MSU ( )
SQL-Tutor, MSU ( )
ELM-ART, Trier ( )
Before we go on to the next system we have a look at the evolution in some systems developed by the people who created ISIS-Tutor. ISIS-Tutor is a descendent of ITEM/IP, developed at the Moscow State University. The evolution continued with SQL-Tutor, developed by Tanja Mitrovic who moved to the University of Canterbury in New Zealand, and with ELM-ART, developed by a group including Peter Brusilovsky, Gerhard Weber, Alfred Kobsa and Marcus Specht at the University of Trier in Germany. Whereas ISIS-Tutor was still developed in Pascal, Both SQL-Tutor and ELM-ART were developed in Lisp, which is not surprising for ELM-ART because that is a Lisp-tutor. Interbook is a decendent of ELM-ART, developed by Peter Brusilovsky, John Eklund and Elmar Schwartz, when Brusilovsky moved to the Carnegie Mellon University in Pittsburgh, USA. Interbook is still implemented in Lisp. The next version of Interbook however is expected to be based on the AHA! engine in a new version that is still to come.
InterBook, CMU ( )
ELM-ART II, Trier ( )

16. SQL Tutor Knowledge-based tutor for the SQL language
based on constraint-based modeling
currently deals only with the SELECT statement
users register with an initial knowledge level
system suggests problems based on the knowledge level (based on which clause select, from, where, group by, having or order by the user needs to practice
system was evaluated to find out whether it was useful and pleasant to use
SQL-Tutor is described (and sometimes accessible) at:
The SQL Tutor is a tutoring system based on constraint-based modeling. Over 500 constraints are used.
Only the SELECT statement is used in SQL Tutor, but that is no serious restriction as the SELECT statement is needed most.
Users can indicate their initial knowledge level.
As the user learns the system keeps track of which clause (select, from, where, group by, having and order by) the user knows least, and suggests exercises that trains that clause.
We have a demo of SQL Tutor. Sometimes SQL Tutor may be available over the Web, but the site may be down.

17. SQL Tutor, Main Window This is the main SQL Tutor window.
You first select a database and then either a problem or let the system suggest a problem.
The correctness of the solution is evaluated and used to determine what you should study next.
While this is adaptive behavior, SQL Tutor is not really a hypermedia or even hypertext system.

18. Interbook tool for adaptive electronic textbooks:
authoring through Microsoft Word (+conversion tools)
domain model: concepts and prerequisite relationships
user model: overlay model, updated through “outcome concepts” of read pages
adaptive link annotation
several additional tools: index, glossary, “teach me”
a good description of Interbook:
Interbook (development) can be tried at the following address:
Interbook is a tool for what Brusilovsky calls “adaptive electronic textbooks”. The meaning of this is that the basis is a hierarchically structured textbook with chapters, sections, subsections and pages, in which adaptive link annotation is used to guide the student through the learning material. The textbooks are hypertexts in the sense that there are also links in the pages.
The domain model in Interbook consists of concepts to be learned, and prerequisite relationships between these concepts.
When the student studies pages the knowledge is increased. This is represented through an overlay model.
Interbook offers several tools to help users study the course material. These include an index of terms, a glossary and tools to help students find the material needed to study a concept. We briefly show the different tools through some screen shots.
The Interbook system is available on-line, both in a “production” and a “development” version.

19. Interbook: textbook window
The textbook window is the main window in Interbook. It is split and truncated to improve readability.
A section of the ACT-R course material is shown. The Concept bar (right) shows prerequisite and outcome concepts for the section. The Navigation center (top) let the user move in one click to any section on the same or upper level. Colored balls (up) and checkmarks (right, on the concept bar) provide adaptive annotation. The button "Teach me" provides direct guidance. It leads to a page with links to all the pages that provide the required prerequisite knowledge.
The “back” and “continue” buttons allow students to navigate linearly. The “continue” button is not adaptive. It always leads to the next page, whether that is ready to be studied or not.
Interbook distinguishes different knowledge levels: not known, learned, well learned and well known. These are represented as real numbers: 0, 1, 2 and 3. When you read a page that is ready to be learned the knowledge of the concept becomes 1. Reading a second page about the concept makes it 2, and taking a test makes it 3. Reading a page that is not recommended increases the knowledge with just 0.1.

20. Interbook: Glossary and Concepts
This screenshot shows the Glossary and Concept window of Interbook. It is split in two to improve the readability but it is normally just one window.
You first click on a letter in the alphabet. Interbook then shows all the concepts starting with that letter. The colored balls indicate the status of these concepts: green for ready to be learned, red for not ready, and white for already learned.
When a concept is selected it is described briefly, together with a list of pages that contain knowledge of that concept, and also a list of topics for which the chosen concept is a prerequisite.
It is thus possible through the glossary to find out exactly what needs to be studied in order to learn a certain concept.

21. Authoring for Interbook
Creating an Interbook adaptive electronic textbook is initially done through Microsoft Word.
The textbook must use a hierarchical structure with headers (level 1, 2, 3, etc.) in order for Interbook to convert them to the hierarchical structure of chapters, sections and subsections.
Hidden annotations must be entered between the header and the start of a section. The annotations define the prerequisite and outcome concepts.
The conversion to HTML starts from an RTF-saved document. RTF is then converted to an HTML file which must be named with extension “.inter”. The Interbook server parses this file and creates the textbook pages, student model and all the supporting pages on the fly.
The use of Microsoft Word makes authoring easy in some sense: the author only needs to know Word and the special annotations. On the other hand it also implies that the conceptual structure and the content are mixed.

22. Interbook: Evaluation
Goal: to find a value of adaptive annotation
Electronic textbook about ClarisWorks
25 undergraduate teacher education students
2 groups: with/without adaptive annotation
Format: exploring + testing knowledge
Full action protocol
Results:
Sequential navigation dominates (“continue” button)
Adaptive link annotation encourages non-sequential navigation
Most students follow the “green” links
Interbook has been evaluated to find out what the value is of using adaptive annotation.
An electronic textbook about ClarisWorks was given to 25 undergraduate teacher education students.
The students were split into two groups: one was given a version with adaptive annotation,
the other a non-adaptive version.
The students were asked to explore the textbook and then there knowledge was tested.
All the students’ actions were logged.
The results first of all show that students more often than not use the “continue” button,
which leads them through the material in a sequential and non-adaptive way.
In the cases where links were followed the adaptive annotation was found to be followed:
most students tend to follow the “green” links.

23. Intrigue: adaptive tourist guide
Allows for the planning of a trip
stereotype user modeling
allows to plan a trip for a diverse group, for instance parents with children
takes physical disabilities into account, age, interests, etc.
can produce output in html or wml (for mobile phone)
can be tried at:
INTRIGUE is an example of a non-educational adaptive system. It is in fact only adaptable: it bases the adaptation on parameters that you set when you register.
Intrigue lets you plan a vacation, taking into account the available time, your interests, the interests of another group, for instance children, physical disabilities such as difficulty to move or eyesight problems, and geographic interest.
Different presentation formats are available, for instance a separate list of recommendations for you and for the other group, a combined list with explanations as to why the site is recommended for you or the other group, and presentations for handheld devices as well.
Intrigue can be tried on-line to get a feel for what it does.

24. Intrigue: recommendation for 2 groups
This is a presentation of recommendations for two groups of people (yourself and children).
The stars indicate how well-suited the chosen sites are.

25. Intrigue: combined recommendation
In this combined listing an explanation is given with pro and cons for the different sites for the different subgroups.
Note that the advice is all generated automatically.

26. TV Scout: Personalized TV Guide
A cooperation between GMD-IPSI and
Goal: Help users in creating their personal TV schedule
Short-lived data (not a static database)
Low user effort required to “tune” the system
Filtering based on time and genre, information provided by the stations
Users plan only for one day
TV Scout has a simple and an advanced interface, with possibilities for collaborative filtering.
TV Scout is/was an experimental personalized TV Guide. It was developed at GMD in cooperation with the German TV TODAY magazine. The goal of the project was to help users in creating their personal TV schedule for a day or a week.
In contrast with educational or tourist applications, the data is short-lived. Every day new data may be added to the database, and data about past programs becomes obsolete.
It is essential that the system require very little effort on behalf of the users.
Filtering of programs can only be done based on information provided by TV Today, and that is in essence what the TV stations provide.
Since users plan only for one day or so the system concentrates on providing recommendations for “today”.

27. TV Scout: What’s on Tonight?
This viewgraph shows the simple user interface for selecting times and stations.
You select dates and times, and then press Start.
The result can be presented as a list or table. Colors are used to indicate genre and relevance of programs.
The hue determines the genre; the color intensity the relevance. In the list a “star” rating is also given for movies.

28. TV Scout: Setting Preferences
Preferred genres can be indicated
Deeper genres are more specific
Less general than Boolean combinations
The user can indicate preferred genres and store them in the system. A search can be performed based on some extra genres as well.
The genres form a hierarchy with several levels of detail.
You can only include genres with checkmarks. It is not possible to form complex Boolean combinations of terms in a query.

29. TV Scout: Forms and Graphical Interface
TV Scout lets users give more precise indicators of their preferences, through a forms-based or graphical interface.
We won’t go into detail of these possibilities here.
It is possible to rate programs, to use collaborative filtering, and to request a number of the “best” programs in a genre to be recommended per week.

30. TV Scout: Evaluation / Feedback
Orientation is easy, but undo is missing
For some users the system is still too complex (opening folders, buttons to small for visually impaired users)
People liked the „grocery list“ (forms interface)
Overall it is useful and easy to use
High fun-factor!
Biggest success indicator is repeat visits by users
TV Scout has received quite a bit of feedback from users. Overall the system is easy to use, except that there is no undo feature. It is easy to mess up your profile and no way to get it back.
The interface uses small icons and buttons which makes it hard to use for elderly people or people with visual or motor disabilities.
The forms interface or “grocery list” is preferred over the graphical one.
Many visitors have used the system over a period of time, indicating that they liked the system well enough to keep using it.
Unfortunately, as with many research systems, there seems to be no currently active TV Scout system anymore.

31. HERA: Presentation Generation in WIS
Automatic generation of hypermedia interface for data from integrated sources
adaptation to user preferences
adaptation to platform capabilities (devices)
generation of a whole website based on these preferences
integration of heterogeneous data sources: adaptation for the “deep Web”
A demo version is available (but not publicly because of copyright issues on the data).
HERA is the name of the Adaptive Web-Based Information System Research at our university.
In Hera the information is assumed to come from different heterogeneous sources. An example of this would be a set of databases from museums, accessible over Internet.
The first step in Hera is the virtual integration of these data sources.
Hera then allows querying and navigation through the data, using presentations that select information based on a user profile and present it using preferences and device capabilities.
We briefly look at some aspects of Hera and give a demo.

32. Hera Design Methodology
The Hera design methodology stems from RMM, the Relationship Management Methodology, developed by Isakowitz and others. The basis is a data model much like the entity-relationship model in which relationships have a navigational meaning. In Hera there is first a Semantic Layer that deals with semantic data integration. In the Application Layer the data is structured into a hypermedia form and the hypermedia structure is adapted according to the user profile. The generation of the presentation then takes the capabilities of the user’s platform into account.

33. Hera: Conceptual Model Example
This viewgraph shows an example of a conceptual model. The example deals with art information. It talks about techniques that are exemplified by artifacts, created by creators. At a lower level of abstraction the artifacts can be paintings and the creator is a painter. This kind of data model does not indicate how it needs to be translated into a hypermedia structure.
In Hera the conceptual model is expressed in RDF(S).

34. Hera: Application Model Example
The application model expresses a navigational view over the conceptual model. It describes the hypermedia aspects.
Slices are meaningful presentation units, drawn like pizza slices. They are associated to concepts from the conceptual model and contain properties and attributes, and possibly other slices.
Slices are linked together through slice relationships. There are aggregation relationships like index, tour, indexed guided tour etc., and reference relationships like links with a specified anchor.
In Hera the application model is encoded in RDF(S)

35. Hera: Adaptation Model Example
The Adaptation model captures two kinds of adaptation:
Adaptability takes into account the situation in which the user will use the presentation (e.g. the browsing platform)
Adaptivity means that the presentation changes itself according to the “state of the user’s mind” while being browsed
The Adaptation model consists of:
a Device/User Profile that captures “static” visual and platform preferences encoded in CC/PP
a User Session that represents the dynamic user’s state, for instance, did the user visit (or learn) this slice (or concept)
the Application and Update Rules describe the behavior of the presentation (for instance conditional inclusion of slices) and keep the User Session up-to-date (through AHAM-like rules)

36. Hera: Presentation Model Example
The Presentation Model is based on the concept of region which contains attributes and possibly other regions.
Each region is associated with a rectangular screen area.
Slices are translated to regions; one slice can be mapped to several regions.
Slice relationships are materialized with:
Navigational relationships
Spatial relationships
Temporal relationships

37. Hera: Presentation Example
This viewgraph shows two presentations of the same concept. The Web presentation contains an image and a long description, whereas the phone presentation only has the title, year and painter name.

38. The ARIA Photo Agent (video)
Adaptive Linking between Text and Photos
Text is used for searching as it is typed
Text is matched with photo descriptions
keywords, people, place and time
Database with “common sense” used
Adaptive sorting (of photos = search results)
Automatic annotation of selected photos
Annotation (conceptual descriptions) of photos can be manually updated
Project webpage:
The ARIA Photo Agent is a good example of a system that performs adaptation without any predefined rules or concept relationships. The construction of the rules is basically part of the adaptation process itself.
ARIA uses a combination of interesting techniques, and its power lays in this combination:
First of all, ARIA works as some kind of search engine. As the user types a story that story is interpreted as a search query. In particular, the most recently added words or the words where the user is editing are used most for searching.
The search retrieves photos based on the annotations of the photos. There is adaptive sorting of the photos.
The search greatly benefits from a logical inference system and a database with common sense information. In the paper and video demonstration we see a story about Ken and Mary’s wedding. Common sense indicates that Mary is the bride and Ken is the groom. There is a lot of inference using common sense to deduce a lot of information about this wedding.
Photos are also selected based on place and time. Recent photos have a high chance of being relevant because the story is likely about a recent event.
When a photo is selected, the context in which it is placed is used to update the annotation of the photo.
The Aria Photo Agent is nicely illustrated by a video made by the author, Hugo Liu, from MIT.

39. ARIA Screenshot
This screenshot shows the different parts and functions of ARIA, used for sending an .