INFSCI 2955 Adaptive Web Systems. Systems
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1 INFSCI 2955 Adaptive Web Systems Session 1-2: Adaptive E-Learning Systems Peter Brusilovsky School of Information Sciences University of Pittsburgh, USA
2 Overview The Context Technologies Adaptive E-Learning Systems vs. Learning Management Systems (LMS) Why Adaptive E-Learning? Adaptation was always an issue in education - what is special about the Web? greater diversity of users user centered systems may not work new unprepared users traditional systems are too complicated users are alone limited help from a peer or a teacher
3 Technologies Origins of AEL technologies ITS Technologies AH Technologies Native Web Technologies Origins of AEL Technologies Intelligent Tutoring Systems Adaptive Hypermedia Systems Adaptive Web-based Educational Systems
4 Origins of AEL Technologies (1) Adaptive Hypermedia Systems Intelligent Tutoring Systems Adaptive Hypermedia Intelligent Tutoring Adaptive Presentation Curriculum Sequencing Problem Solving Support Adaptive Navigation Support Intelligent Solution Analysis Technology inheritance examples Intelligent Tutoring Systems (since 1970) CALAT (CAIRNE, NTT) PAT-ONLINE (PAT, Carnegie Mellon) Adaptive Hypermedia Systems (since 1990) AHA (Adaptive Hypertext Course, Eindhoven) KBS-HyperBook (KB Hypertext, Hannover) ITS and AHS ELM-ART (ELM-PE, Trier, ISIS-Tutor, MSU)
5 Technology Fusion Adaptive Web Adaptive Educational Systems Adaptive E-Learning Origins of AEL Technologies (2) Information Retrieval CSCL Adaptive Hypermedia Systems Machine Learning, Data Mining Intelligent Tutoring Systems Adaptive Hypermedia Adaptive Information Filtering Intelligent Monitoring Intelligent Collaborative Learning Intelligent Tutoring
6 Inherited Technologies Intelligent Tutoring Systems course sequencing intelligent analysis of problem solutions interactive problem solving support example-based problem solving Adaptive Hypermedia Systems adaptive presentation adaptive navigation support How to Model User Knowledge Domain model The whole body of domain knowledge is decomposed into set of smaller knowledge units A set of concepts, topics, etc Student model Overlay model Student knowledge is measured independently for each knowledge unit
7 Simple overlay model Concept 1 yes Concept 2 yes yes Concept 3 Concept 4 no Concept 5 Concept N no no Course Sequencing Oldest ITS technology SCHOLAR, BIP, GCAI... Goal: individualized best sequence of educational activities information to read examples to explore problems to solve... Curriculum sequencing, instructional planning,...
8 Course Sequencing What is modeled? User knowledge of the subject User individual traits What is adapted? Order of educational activities Presentation of hypertext links Presented content Problem solving feedback Active vs. passive sequencing Active sequencing goal-driven expansion of knowledge/skills achieve an educational goal predefined (whole course) flexible (set by a teacher or a student) Passive sequencing (remediation) sequence of actions to repair misunderstanding or lack of knowledge
9 Levels of sequencing High level and low level sequencing Sequencing options On each level sequencing decisions can be made differently Which item to choose? When to stop? Options predefined random adaptive student decides
10 Simple cases of sequencing No topics One task type Problem sequencing and mastery learning Question sequencing Page sequencing Sequencing with models Given the state of UM and the current goal pick up the best topic or ULM within a subset of relevant ones (defined by links) Special cases with multi-topic indexing and several kinds of ULM Applying explicit pedagogical strategy to sequencing
11 ELM-ART: question sequencing SIETTE: Adaptive Quizzes Combination of CAT and concept Based adaptation
12 Models in SIETTE Beyond Sequencing: Generation
13 Adaptive Problem Solving Support The main duty of ITS From diagnosis to problem solving support Highly-interactive support interactive problem solving support Low-interactive support intelligent analysis of problem solutions Adaptive Problem Solving Support What is modeled? User knowledge of the subject User individual traits What is adapted? Order of educational activities Presentation of hypertext links Presented content Problem solving feedback
14 Models for interactive problemsolving support and diagnosis Domain model Concept model (same as for sequencing) Bug model Constraint model Student model Generalized overlay model (works with bug model and constraint model too) Teaching material - feedback messages for bugs/constraints Example: ELM-ART
15 Example: SQL-Tutor Interactive Problem Solving Support Classic System: Lisp-Tutor The ultimate goal of many ITS developers Several kinds of adaptive feedback on every step of problem solving Coach-style intervention Highlight wrong step What is wrong What is the correct step Several levels of help by request
16 Example: PAT-Online Example: WADEIn
17 Problem-solving support Important for WBE problem solving is a key to understanding lack of problem solving help Hardest technology to implement research issue implementation issue Excellent student modeling capability! Adaptive hypermedia Hypermedia systems = Pages + Links Adaptive presentation content adaptation Adaptive navigation support link adaptation Could be considered as soft sequencing Helping the user to get to the right content
18 Adaptive problem solving support What is modeled? User knowledge of the subject User individual traits What is adapted? Order of educational activities Presentation of hypertext links Presented content Problem solving feedback 4 Adaptive Annotation: Icons InterBook system 1. Concept role 3. Current section state 2. Current concept state 4. Linked sections state
19 Demo: QuizGuide Demo: NavEx
20 Adaptive Presentation What is modeled? User knowledge of the subject User individual traits What is adapted? Order of educational activities Presentation of hypertext links Presented content Problem solving feedback Example: SASY Scrutable adaptive presentation
21 Adapting to User Knowledge: Other Ideas Adaptive interface Presence of menus and widgets in an educational applet can be adapted to user knowledge Educational animation and simulation Adaptive explanations Adaptive visualization Demo: Improve
22 Adapting to Individual Traits Source of knowledge educational psychology research on individual differences Known as cognitive or learning styles Field dependence, wholist/serialist (Pask) Kolb, VARK, Felder-Silverman classifiers Style-Adaptive Hypermedia What is modeled? User knowledge of the subject User individual traits What is adapted? Order of educational activities Presentation of hypertext links Presented content Problem solving feedback
23 Style-Adaptive Hypermedia Different content for different style Recommended/ordered links Generated on a page Mixed evidences in favor Different navigation tools for different styles Adding/removing maps, advanced organizers, etc. Good review: Bajraktarevic, N., Hall, W., and Fullick, P Incorporating Learning Styles in Hypermedia Environment: Empirical Evaluation, In Proceedings of Workshop on Adaptive Hypermedia and Adaptive Web-Based Systems, Nottingham, Example: AES-CS Interface for field-independent learners
24 Example: AES-CS Interface for field-dependent learners Style-Adaptive Feedback What is modeled? User knowledge of the subject User individual traits What is adapted? Order of educational activities Presentation of hypertext links Presented content Problem solving feedback
25 Overview: Classic Technologies What? Order of activities Feedback Content Links Knowledge Sequencing Adaptive diagnosis Adaptive presentation Adaptive navigation support Styles? Style-adaptive feedback Adaptive presentation Adaptive navigation support Origins of AEL Technologies (2) Information Retrieval CSCL Adaptive Hypermedia Systems Machine Learning, Data Mining Intelligent Tutoring Systems Adaptive Hypermedia Adaptive Information Filtering Intelligent Monitoring Intelligent Collaborative Learning Intelligent Tutoring
26 Native Web Technologies Availability of logs - helping the teacher! Log-mining Intelligent class monitoring - class progress is available! One system, many users - group adaptation! Adaptive collaboration support Web is a large information resource - helping to find relevant open corpus information Adaptive content recommendation Possible combinations of the above Collaborative recommendation Social navigation What You Can Get from Logs? Log processing and presentation Presenting student progress on topic and concept level: making sense of class Course/site improvements Grouping users by learning styles Intelligent class monitoring Comparing progress, identifying students way ahead and behind
27 Adaptive Collaboration Support Peer help Collaborative group formation Group collaboration support Collaborative work support Forum discussion support Mutual awareness support More information Proceedings of the Workshop on Personalization in E-learning Environments at Individual and Group Level at the 11th International Conference on User Modeling, Personalized Access to Educational Resources A lot of resources are available on the Web and in educational DL/Repostitories A new direction of adaptation - provide personalized access to these resources Content based recommender Adding advantage of community wisdom Collaborative recommender systems Social navigation systems
28 Modeling User Interests Concept-level modeling Same domain models as in knowledge modeling, but the overlay models level of interests, not level of knowledge Keyword-level modeling Uses a long list of keywords (terms) in place of domain model User interests are modeled as weigthed vector or terms Originated from adaptive filtering/search area Keyword User Profiles
29 Use of Profiles in AES: ML Tutor Overview The Context Technologies Adaptive E-Learning Systems vs. Learning Management Systems (LMS)
30 What LMS Can Do For students Course information and content delivery Assessment and grades Communication and collaboration For teachers Authoring Learning control Student monitoring Communication What AES Can Do for Students Presentation Adaptive presentation, adaptive navigation support, adaptive sequencing Assessment Adaptive testing Communication and collaboration Peer help and collaborative group formation Collaboration coach Learning by doing Problem solving support
31 What AES Can Do for Teachers Student monitoring Identifying students in trouble Control Sequencing Adaptive navigation support Authoring Concept-based authoring and courseware engineering AES vs. LMS Adaptive E-Learning systems can provide a more advanced support for most functions Course material presentation - InterBook, AHA Assessment with quizzes - SIETTE Threaded discussions - collaboration agents Student management - intelligent monitoring Why LMS are not really adaptive? Except simple control and learning design
32 Challenges How to make it working in practice? AES systems use advanced techniques - hard to develop AWBES content is based on knowledge - hard to create AES require login and user modeling - hard to integrate Possible solutions - (watch, PhD students!) Component-based architectures for AWBES Authoring support Open Corpus adaptive systems Component-based Architectures Research systems can provide a better support of almost each function of E-Learning process Adaptive systems show how to implement nearly each component adaptively We need the ability to assemble from components Course authors can choose best components and best content for their needs Components providers and content providers have a chance to compete in developing better products
33 Current State Several component-based frameworks ADAPT 2, ActiveMath, MEDEA, Attempts to develop systems with internal components Reusable user/student model servers Some matching work in the standardization movement Re-use/Standards Movement Learning Object Re-use supported by coming standards is another major research direction in E- Learning The re-use movement joins many existing streams of work driven by similar ideas Create content once, use many times Content independent from the host system Content and interfaces with the host system are based on standards (metadata, CMI, etc) Let content providers be players in E-Learning The future is components and re-use
34 ADAPT 2 Architecture Portal Activity Server Value-added Service Student Modeling Server Knowledge Tree II Portal
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