AH Example Systems

Dr. Alexandra Cristeaa.i.cristea@warwick.ac.uk

http://www.dcs.warwick.ac.uk/~acristea/

Example Adaptive Hypermedia Systems• We show examples that are very different:

– TV Scout: personalized TV guide (GMD Darmstadt)– AIMS: Adaptive Information Management System

(TU/e+UT)– SQL-Tutor: Intelligent Tutoring System for SQL (Canterbury,– New Zealand)– ISIS tutor (Moscow State University)– Interbook: Adaptive Electronic Textbooks (Univ. of

Pittsburgh)– INTRIGUE: adaptable tourist guide (Univ. of Torino)– HERA– MOT (My Online Teacher) – Adaptation to learning styles in (an extension of) AHA!– The ARIA Photo Agent (MIT) with commonsense reasoning

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: What’s on Tonight?

TV Scout: Setting Preferences

• Preferred genres can be indicated

• Deeper genres are more specific

• Less general than Boolean combinations

TV Scout: Forms and Graphical Interface

suggest queries

programdescriptionlist

programdescription table

viewing timeprofile editor

channelprofileeditor

video labels

laundry list

QSAprofileeditor

QSA profileeditor (experts)

textsearch

querymenus

QSAmenu

TV Scout user interface with starting page

retentionmenus

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

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.– initiated at and evaluated with students from the Universiteit

Twente

• Note: adaptation to a “moving target”, because the knowledge changes

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

AIMS Student Interface

AIMS Instructor Domain Environment

AIMS Instructor Library Environment

AIMS Instructor Course Environment

AIMS Admin Environment

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:– http://www.cosc.canterbury.ac.nz/tanja.mitrovic/sql-

tutor.html – Try out: http://ictg.cosc.canterbury.ac.nz:8000/sql-tutor/login

SQL Tutor, Main Window

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 to determine learning effect of using adaptation– http://www.cs.joensuu.fi/~mtuki/www_clce.270296/

Brusilov.html

ISIS Tutor with Link Annotation

The wrong example:

ISIS Tutor with Link Removal + Annotation

Evaluation of ISIS Tutor (number of steps)

Evaluation of ISIS Tutor (repetitions)

Relationship between well-known AHS

Interbook• tool for adaptive electronic textbooks (developed

mostly at the Carnegie Mellon University):– 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:– http://ausweb.scu.edu.au/aw97/papers/eklund/paper.htm

Interbook: textbook window

Interbook: Glossary and Concepts

Authoring for Interbook

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

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 sometimes be tried at:– http://silk.di.unito.it:8083/ishtar/intrigue.html

Intrigue: recommendation for 2 groups

Intrigue: combined recommendation

MOT (My Online Teacher)

• Authoring environment based on the LAOS authoring framework that specifies separation of concerns

MOT (old): Domain Concept attribute creation

Current conceptCurrent concept

conceptconceptattributeattribute

Try at: http://e-learning.dsp.pub.ro/mot/

Ordering Ordering ofoflessonslessons

Weights ofWeights ofsublessonsublesson

Labels ofLabels ofsublessonsublesson

MOT (old): Editing a Goal Map

Evaluation of early MOT (2004) Goal point of view evaluation

CollaborationCollaboration - Problems? - Suggestions for solving? How did you do it? - Good points?

CompletenessCompleteness(LAOS two

layer)

- Perceived percentage? expressivity? - (perceived) connectivity degree? Should there

be more connections, or less? What extra connections? What superfluous?

AdaptivityAdaptivity - How much adaptivity to the design goal is perceived?

Design rangeDesign range - How much more can be achieved compared to linear model?

USI point of view evaluation

Ease of useEase of use - information display, information order, - distance of search (depth); - color scheme, ease of access, ease of

installation

RobustnessRobustness - parallelism (data overlap), security, recovery

ComplexityComplexity - analysis of possible reduction.

Semantics in MOT

• MOT is based on LAOS and on semantic web directives

• necessary – more explicit ontologies, – synonyms - to identify semantic overlaps

User Model in MOT

• dynamic model of the user's history;

• user model variables should be also user writable (flag);

• retrieved by prompting the user– Specified where? AM? UM?

MOT: Domain attribute creation

Current domain conceptCurrent domain concept

Domain Domain conceptconceptattributeattributeTry at: http://prolearn.dcs.warwick.ac.uk/MOT/

MOT: Uploading other files/content

Ordering Ordering ofoflessonslessons

Weights ofWeights ofsublessonsublesson

Labels ofLabels ofsublessonsublesson

MOT: Goal model authoring

Group Group ofofSub-Sub-lessonslessons

GroupGroupalternativesalternatives

MOT: Goal model authoring

Evaluation of new MOT (2007)

• intensive two-week course AH & SW • 33 out of 61 students selected: 4th year

Engineering & 2nd year MsC in CS• theoretical exam half way for selecting

students due to space constraints in computer room

• at the end: practical exam & 5 questionaires• 3 systems: OLD MOT, NEW MOT &

Sesame2MOT

Questionnaires

• SUS questionnaire for comparing usability

• Multiple choice questionnaire constructed direct questions based upon division of main hypotheses

Second-Order Adaptation• Most systems adapt to one parameter:

– Recommender systems adapt to what they think the user’s interests are

– Learning systems adapt to what they think the user’s knowledge is about certain concepts

– Some systems can perform adaptation to devices or network performance

• More advanced systems adapt to more than one parameter at once– We look at the adaptation to learning styles in an

already adaptive learning application

LAG-XLS: an XML Learning Style Adaptation Language

• Based on the LAG language

• Elements of the language:– select – selecting concept representation– sort – sequencing concept representation– setDefault – setting defaults– action – updating the User Model

Information aboutVerbalizer/Vizualizer(Imager) LS

b

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:– http://web.media.mit.edu/~lieber/Lieberary/Aria/Aria-Intro.html

ARIA Screenshot

Video at: http://web.media.mit.edu/~lieber/Lieberary/Aria/Commonsense-Aria-Demo.swf

Any questions?