Student-adaptive educational systems

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Student-adaptive educational systems

• Haiying Deng
• ICS UCI

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Papers for today

• Methods and techniques of adaptive hypermedia
  (Brusilovsky, P)
• MetaDoc An Adaptive Hypertext Reading System
  (Boyle, C. and Encarnacion)
• Using Bayesian Networks to Manage Uncertainty in
  Student Modeling (Conati, C. et al )

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Methods and Techniques of AH

• Peter Brusilovsky
• HCII, School of CS Carnegie Mellon University

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Outline

• Overviews of AH
• Methods and techniques of Content Adaptation
• Methods and techniques of Adaptive navigation
  support

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Definition of AH

• All hypertext and hypermedia systems
• which reflect some features of the user in the
  user model and apply this model to adapt various
  visible aspects of the system to the user.

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• Adaptation techniques refers to methods of
  providing adaptation in existing AH systems.
• Adaptation methods are defined as generalizations
  of existing adaptation techniques.

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Adapting to what

• Knowledge overlay model or stereotype model
• Users goal similar to the overlay model
• hierarchy (a tree) of
  tasks
• Background and experience
• preference

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Methods of content adaptation

• Additional explanations
• Prerequisite explanations
• Comparative explanations
• Explanation variants
• Sorting (the fragments of info by the relevance)

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Techniques of Content Adaptation(1)

• Lower level conditional text
• all possible info is divided into several chunks
  of texts, which is associated with a condition on
  the level of the user
• the info chunk presented only when the condition
  is true
• ITEM/IP, Lisp-Critic, C-book

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Techniques of Content Adaptation(2)

• Higher level stretchtext
• replace the activated hotword extending the text
  of the current page.
• Collapse the non-relevant stretchtext extension,
  uncollapse the relevant ones.
• Collapsed and uncollapsed hotwords can be
  transferred with each other
• KN-AHS

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Techniques of Content Adaptation(3)

• page variants techniques two or more variants of
  the same page with different presentations of the
  same content for different user according to the
  user stereotype ORIMUHS, WING-MIT,
  Anatom-Tutor, C-book.
• Fragment variants variants of explanations for
  each concept -- Anatom-Tutor
• Combination of the two above Anatom-Tutor

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Techniques of Content Adaptation(4)

• Frame-based technique info about a concept in
  form of a frame, frames forms a slot, slots forms
  a scheme. Slots or schema chosen by some rules.
• Hypadapter and EPIAIM
• PUSH a combination of stretchtext and
  frame-based technique, which has its own entity
  type of info, similar to frame-based model and a
  interface similar to MetaDoc stretchtext
  interface.

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Methods of adaptive navigation support(1)

• Global guidance
• give suggestion at each step of browsing about
  the next link WebWatcher
• Adaptively sort all the links from the given node
  according to the global goal Adaptive HyperMan
  and HYPERFLEX

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Methods of adaptive navigation support(2)

• Local guidance
• Similar to the global guidance, but different in
  terms of the local goal, based on the
  preferences, knowledge and background

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Methods of adaptive navigation support(3)

• Local orientation support to help the user in
  local orientation
• - providing additional info about the current
  node
• - Limiting the navigation opportunities and let
  user concentrate on the most relevant links

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Methods of adaptive navigation support(4)

• Global orientation support
• Help understand the overall structure of the
  hyperspace and the users absolute position.
• Instead of visual landmarks and global maps
  directly, provide more support by applying hiding
  and annotation technology.
• Providing different annotation based on the
  knowledge level.

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Methods of adaptive navigation support(5)

• Managing personalized views
• Protect users from the complexity of the overall
  hyperspace by organizing personalized
  goal-oriented views, each of which is a list of
  links to all relevant hyper documents
• BASAR

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Techniques of adaptive navigation support(1)

• HYPERFLEX provides with global and local
  guidance by displaying an ordered list of related
  nodes.
• Adaptive HyperMan inputs including user
  background, search goal interest of current node,
  etc, outputs an ordered set of relevant doc.
• Hypadapter use a set of rules to calculate the
  relevance of links for each slot.

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Techniques of adaptive navigation support(2)

• HyperTutor and SYPROS use rules to decide the
  visible concepts and nodes based on the concept
  types, the types of links to other concepts and
  the current state of users knowledge.
• Hynecosum supports both goal-based and
  experience-based methods of hiding using
  hierarchies of tasks.

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Techniques of adaptive navigation support(3)

• ISIS-Tutor, ITEM/PG and ELM-ART support several
  methods of local and global orientation support
  based on annotation and hiding, links to the
  concepts with different educational states are
  annotated differently using different colors.
• HYPERCASE only known example of map adaptation
  supports local and global orientation by adapting
  the local and global maps

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Summary

• Identified seven adaptation technologies for AH
• adaptive text presentation
• Adaptive multimedia presentation
• Direct guidance
• Adaptive sorting
• Hiding
• Annotation of links
• Map adaptation

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MetaDoc An Adaptive Hypertext Reading System

• Craig Boyle
• Antonio O Encarnacion

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Overview

• Simple online text documentation fixed
  organization.
• Hypertext present through link selection
• Adaptive Hypertext actively participate the
  reading.

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Adaptivity

• Extends the conventional flexibility of the
  hypertext from the network level to the node
  level.
• MetaDoc Stretchtext

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Example Stretchtext
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User model

• Adapts to the reader, instead of a document
• Contains a representation of the readers
  knowledge.
• Participates in the reading process.

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Related work

• Stretchtext (Nelson, 1971)change the depth of
  the information in a node.
• Stretching replace the whole node , similar to
  GOTO links
• Replacement-buttons
• DynaText limited form of stretchtext.

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MetaDoc to other doc forms

• User Modeling active document
• Stretchtext three dimensional reading and
  writing
• Hypertext non-sequential reading and writing
• Online Documentation hierarchical retrieval
• Printed Text linear reading and writing

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Interactive Agent

• Store the knowledge about the reader
• Used to vary the level of detail in the doc.

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• User level and levels of information
• Users and Stereotype novices, beginners,
  intermediates or experts based on the knowledge
  of Unix/AIX and general computer concepts.
• Concept levels the same as above.
• MetaDoc varies the amount of explanation or
  detail info to present the correct level of info
  based on the internal stereotype info of a
  concept and the readers knowledge level.

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MetaDoc document

• Choose different versions of a single node
  manually or automatically
• Selectively adjust parts of the node instead of
  adjusting the whole node

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Writing Stretchtext

• Smooth transition
• Familiar landmarks for different levels
• Common node identifiers
• Be ordered

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Stretchtext in MetaDoc

• Vary the info in terms of either explanation or
  amount of detail
• Choose the embedded and appended stretchtext
  less confusing
• Selected by mouse operations which is
  context-sensitive and recursive

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Default presenting rules

• Explanation of concepts associated with higher
  levels are automatically provided for lower level
  users.
• Explanation of concepts associated with lower
  levels unnecessary for higher level users are
  suppressed.
• Higher level details not necessary for
  understanding a concept are suppressed for lower
  level users
• Details of equal or lower level concepts are
  automatically displayed for higher level users.

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Architecture of MetaDoc(1)

• 3D Document component determines the final form
  of the node presented to the user and receives
  commands from the user, composed of the Document
  Presentation Manager and the Base Document

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Architecture of MetaDoc(2)

• Intelligent Agent dynamically keeps track of the
  user knowledge level, automatically matching the
  presented info depth to the user level, composed
  of a user model and the inference engine
• Domain Concepts bridge the gap between the above
  two

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User Modeling

• Explicit modeling give user the option of
  explicitly changing the user model within the
  session
• Implicit modeling stretchtext operation request
  for more or less explanation command for less or
  more detail

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Evaluation MetaDoc

• Evaluated with respect of comprehension and
  location of specific info.
• Compared three systems MetaDoc, hypertext-only
  and stretchtext versions.

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MetaDoc evaluation
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Discussion of results

• Users of AH doc spent less time answering the
  comprehension questions correctly
• Users of adaptive documents spent less time
  answering search and navigation questions
• MetaDoc had greater impact on novice users than
  experts.

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Conclusion

• MetaDoc provides an environment in which the user
  read a hypertext document that will adapt to
  his/her needs.
• Can Help improve readers performance.

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Using Bayesian Networks to Manage Uncertaintyin
Student Modeling

• CRISTINA CONATI
• ABIGAIL GERTNER and KURT VANLEHN

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Andes systems main contribution

• Provides a comprehensive solution to the
  assignment of credit problem for both knowledge
  tracing and plan recognition
• supports prediction of student actions during
  problem solving,

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Problem solving interface

• Provides two kinds of help
• Error help
• Procedural help

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Example studying interface

• Under SE-Coach which gets the students to
  self-explain examples
• Step correctness by Rule Browser
• Step utility by Plan Browser

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Andes approach to student modeling
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Issues in real world(1)

• 1. Context specificity
• 2. Guessing
• 3. Mutually exclusive strategies
• 4. Old evidence
• 5. Errors

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Issues in real world(2)

• 6. Hints
• 7. Reading latency
• 8. Self-explaining ahead
• 9. Self-explanation menu selections

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Networks of Andes

• Data structure solution graph
• Knowledge-based model construction approach
• For problem solving all the correct solution
• For example studying one single solution

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• R-try-Newton-21aw
• if the problems goal is to find a force
• then set the goal to try Newtons second Law to
  solve the problem
• R-normal-exists
• If there is a goal to find all forces on a body
• And the body rests on a surface
• Then there is a Normal Force exerted on the body
  by the surface.

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Encodings

• Givens (SCALAR (KIND MASS)(BOD Y BLOCK-A)(MAGNI
  TUDE 50)(UNITS KG))
• Problem goal (GOAL-PROBLEM (IS
  FIND-NORMAL-FORCE)(APPLIED-TO BLOCK-A)(APP
  LIED-BY TABLE)(TIME 1 2))
• Sub-goals

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Structure of the networks(1)

• The domain-general part represents student
  long-term knowledge

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Structure of the networks(2)

• Task-specific part

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Modeling for Problem Solving

• Errors of Omission and Errors of Commission
• Updating the student model after a hint
• Using the network to generate help

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Modeling for Example Studying(1)

• P(RA T all parents T) 1 - a
• address the issue of self-explaining ahead
• represents a students tendency to self-explain
  an inference as soon as she has the knowledge to
  do so

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Modeling for Example Studying(2)

• The students reading time Low, ok, long
• The longer to view an example item, the higher
  prob. to self-explain it
• P(RAT RuleT, All preconditionsT, Read ?
  LOW,OK) 1 - a

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Modeling for Example Studying(3)

• Use of the self-explanation Menus the higher the
  number of wrong attempts, the higher the P(SET
  Context-rule F), which implements that in this
  situation it is more likely to achieve the
  correct action through random selection in the
  tools rather than reasoning

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Modeling for Example Studying(4)

• Use the student model to support
  self-explanation if the model contains the
  certain proposition nodes with prob. Lower that
  the threshold for self-explanation, prompt the
  students to explain further or read the lines
  more carefully

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Evaluation of Andes

• Machine learning style evaluation 65
• Evaluation with real students 1/3 of a letter to
  1 letter grade better than the control group
• Evaluation of the student model for example
  studying

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Discussion

• Empirical evaluations of the resulting coaches
  indicated that students learned more with them
  than with conventional instruction.
• How did Andes achieve the success accurately
  represent the probabilistic dependencies in the
  task domain.