Student-adaptive
educational systems
Haiying Deng
ICS UCI
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 )
Methods and Techniques of
AH
Peter Brusilovsky
HCII, School of CS Carnegie
Mellon University
Outline
• Overviews of AH
• Methods and techniques of Content
Adaptation
• Methods and techniques of Adaptive
navigation support
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.
• Adaptation techniques refers to methods
of providing adaptation in existing AH
systems.
• Adaptation methods are defined as
generalizations of existing adaptation
techniques.
Adapting to what
– Knowledge: overlay model or stereotype
model
– User’s goal: similar to the overlay model
hierarchy (a tree) of tasks
– Background and experience
– preference
Methods of content adaptation
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Additional explanations
Prerequisite explanations
Comparative explanations
Explanation variants
Sorting (the fragments of info by the
relevance)
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
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
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
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.
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
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
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
Methods of adaptive navigation
support(4)
• Global orientation support
– Help understand the overall structure of the
hyperspace and the user’s 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.
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
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.
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 user’s
knowledge.
• Hynecosum: supports both goal-based and
experience-based methods of hiding using
hierarchies of tasks.
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
Summary
• Identified seven adaptation technologies for AH:
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adaptive text presentation
Adaptive multimedia presentation
Direct guidance
Adaptive sorting
Hiding
Annotation of links
Map adaptation
MetaDoc: An Adaptive
Hypertext Reading System
• Craig Boyle
•Antonio O Encarnacion
Overview
Simple online text documentation: fixed
organization.
Hypertext: present through link selection
Adaptive Hypertext: actively participate
the reading.
Adaptivity
• Extends the conventional flexibility of the
hypertext from the network level to the
node level.
• MetaDoc: Stretchtext
Example: Stretchtext
User model
• Adapts to the reader, instead of a
document
• Contains a representation of the reader’s
knowledge.
• Participates in the reading process.
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.
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
Interactive Agent
• Store the knowledge about the reader
• Used to vary the level of detail in the doc.
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 reader’s knowledge level.
MetaDoc document
• Choose different versions of a single node
manually or automatically
• Selectively adjust parts of the node
instead of adjusting the whole node
Writing Stretchtext
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Smooth transition
Familiar landmarks for different levels
Common node identifiers
Be ordered
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
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.
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
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
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
Evaluation MetaDoc
• Evaluated with respect of comprehension
and location of specific info.
• Compared three systems: MetaDoc,
hypertext-only and stretchtext versions.
MetaDoc evaluation
Reading comp. time (sec.)
Reading comp. correct
Mean search time (sec.)
Hypertext
Stretchtext
MetaDoc
Expert Novice Expert Novice Expert Novice
1780
1930 1250 1780
810 1420
5
3
6.5
7
7
7
755
725
645
530
555
575
Significant results of MetaDoc evaluation
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.
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.
Using Bayesian Networks to
Manage Uncertainty
in Student Modeling
• CRISTINA CONATI
• ABIGAIL GERTNER and KURT VANLEHN
Andes system’s 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,
Problem solving interface
• Provides two kinds of help:
– Error help
– Procedural help
Example studying interface
• Under SE-Coach which gets the students
to self-explain examples
– Step correctness: by Rule Browser
– Step utility: by Plan Browser
Andes’ approach to student
modeling
Issues in real world(1)
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1. Context specificity
2. Guessing
3. Mutually exclusive strategies
4. Old evidence
5. Errors
Issues in real world(2)
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6. Hints
7. Reading latency
8. Self-explaining ahead
9. Self-explanation menu selections
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
R-try-Newton-21aw:
if the problem’s goal is to find a force
then set the goal to try Newton’s 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.
Encodings
• Givens: (SCALAR (KIND MASS)(BOD Y BLOCKA)(MAGNI TUDE 50)(UNITS KG))
• Problem goal: (GOAL-PROBLEM (IS FINDNORMAL-FORCE)(APPLIED-TO BLOCK-A)(APP LIEDBY TABLE)(TIME 1 2))
• Sub-goals
Structure of the networks(1)
• The domain-general
part: represents
student long-term
knowledge
Structure of the networks(2)
• Task-specific part:
Modeling for Problem Solving
• Errors of Omission and Errors of
Commission
• Updating the student model after a hint
• Using the network to generate help
Modeling for Example Studying(1)
• P(RA= T | all parents = T) =1 - a
– address the issue of self-explaining ahead
– represents a student’s tendency to selfexplain an inference as soon as she has the
knowledge to do so
Modeling for Example Studying(2)
• The student’s reading time: Low, ok, long
– The longer to view an example item, the
higher prob. to self-explain it
– P(RA=T | Rule=T, All preconditions=T, Read Є
{LOW,OK}) =1 - a
Modeling for Example Studying(3)
• Use of the self-explanation Menus: the
higher the number of wrong attempts, the higher
the P(SE=T | 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
Modeling for Example Studying(4)
• Use the student model to support selfexplanation: 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
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:
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.