Information Architecture of
Interactive and Customizable
Learning Environments
Myeongjin Lee
Adviser : Geoffrey Fox
NPAC
Computer and Information Science
Syracuse University
The Learning Environment
• To design a computerized learning environment is
interdisciplinary, spanning such areas as HCI,
Cybernetics, Cognitive Psychology, Human factor
engineering, and education.
• Computer scientist is an architect to design/build
the house of learning environment, to encompass
required functionalities by a customer (a learner
and a teacher)
The Learning Environment
• Computer Science
Cybernetics
HCI
Education
Human factor
engineering
Learning
Environment
Computer
Science
Cognitive
Psychology
Interaction Model (Wegner 1997)
• Algorithms vs. Interaction Model
• Interactive systems provide history-dependent services
over time that can learn from and adapt to experience.
• Interaction Machines : Turing machines + input and output
actions that support dynamic interaction with an external
environment.
• Interaction machines to model practical applications!
Learning Software and Batteries
• Computerized performance evaluation tools - e.g. to
measure motor skills, response time. Limited because they
can not be changed dynamically and seldom customizable.
• Computerized learning tools - ordering customized
software is not easy. User performance may change in
different ratio according to each user.
• Lack of functionalities in observing user events’ flows.
The System
• Cybernetics (the ancestor of Computer Science) : the
nature and concepts of a system.
• We use their terms to design computerized learning
environment (instrumental learning) based on “real
learning process”
• Requirements of a successful learning is also applied to the
case of instrumental learning.
The System
The Control System
Adaptive Control System
• The adaptive control system can change the relation
between a user and system to achieve a more specific
object as time goes on.
• The learning system requires a control of user inputs with
adaptive ability.
• A learning machine is an advanced instance of an adaptive
control system.
Teaching Machine
• To interact with students in order to teach or aid in their
learning
• The first teaching machine : S. L. Pressy 1920
– First, it was an automatic test administrator, then “learning” mode
was added.
• Norman Crowder, and Skinner’s teaching machines
• Skinner contended that learning through a programmed
environment is more effective.
• An adaptive controller can provide flexible model of
teaching machine. (above ones are fixed environments)
Discrete Event System
• A dynamic system whose state space is a discrete set,
where the state transition mechanism is event-driven.
– A discrete set
– an event-driven state transition mechanism
• a stochastic discrete event system : a system with timing
information and an uncertainty factor regarding event
prediction
a learning system!
An Interactive Customizable Information
Architecture for a Learning Environment
• Internet : Collection of available and extensible (scalable)
components and services.
• Information architecture to build and design a learning
environment on the Internet.
• Also to support “exploratory learning”
• The adaptive control system is an abstraction of such
architecture.
• An architecture can be viewed as an interaction model.
Components
• Application Server (AS), Server, and Client
Application Server
• To render custom interfaces
– separation of data presentation from its contents
• To get user inputs to process
– most of the work is processed at the server side.
– AS processes the trapped user events and sends them to a server
• To update or custom user environments
– registration, customization, or update requests from clients
– interfaces or protocol to use pre-existing web-based software
Server
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A Content Server.
To communicate with databases
To process data from the AS or from a database
object repository
Client
• A user ( the learning environment)
• A student or a teacher
• Clients generate user events which are tracked and
processed at both client and server sides.
Services
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Our information architecture provides education services
AS is the middle tier
Content server and database are the back-end
Described here are required services at our information
architecture for a learning environment
• Properties are the requirement of each service.
http://www.npac.syr.edu/DC
“Full Description of Web Flow and Friends
for Education and Science portals”
Education Portals
• Not one-size-fits-all approach, but
customizable objects
• Links to other sites/games/softwares can be
added/deleted (adaptiveness).
• Portal Objects are handled by services of
the Information Architecture.
Education Portals Continued
• Our content-based analysis can be “required
/ recommended features” to be good
education portals.
• “Assume that we are building education
portals in terms of Distributed Education
Objects” (http://www.npac.syr.edu/DC/)
• OSS (Nasdaq:WEBB), WebCT,
Blackboard..
Services at the Information
Architecture
Event Service
• To track and analyze client inputs or events
• User events need to be captured at both client and server
sides!
– User performance at the learning session
– System performance may make user perceive information
differently.
• Basic functions to analyze crude data at the AS reduce
workload at Content server.
Data Rendering Service
• This service renders different types of data to client’s
interface.
• Content server or object repository does not have to know
the representation of data in the information processing
procedure.
• The user interface rendered by the AS should have event
gathering features to be used by event service.
Administrative Service
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Registration of users
Monitoring of user activity
the customization of user environment
software module to include pre-existing application into
the learning system
Content Service
• Content Service concerns the repository requirement for
different performance tests, batteries, or learning
application.
• The repository needs to be scalable.
• To add new required features should be easy to program.
• To be modular in terms of functionalities
• Performance test or learning application can well be
implemented over such information architecture!
Database related service
• Connection between the content server and databases
Services and their properties
An example of interaction among
components
Conclusion
• To transform the Internet into an intelligent learning
environment
• An AS : customization, rendering data, and tracking user
inputs.
• Possibly several content servers and the AS architecture
provide simple and easy-to-follow 3 tier architecture for
interactive learning environment.
• The use of XML : easy personalization, presentation of
information in richer way.
Smart Desk
• A web-based interactive learning environment.
• Initially designed by Dr. Warner for a patient who was
cognitively disabled from early brain seizure.
• Also a hardware environment to explore or develop a
user’s physical capability.
Hardware Interface Example 1
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Track ball for a mouse movement.
Graphic Tablet for a mouse movement
Other objects with photo sensors
Designed by Matt Carbone
Hardware Interface Example 2
• Smart Desk Chair designed by Tim Lauring
• 8 analog signals from pressure sensors and 12 digital
signals from palm mouse.
Features
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Customizable
Adaptive
Scalable
User tracking mechanism (event detection)
Customizable
• To choose from list of applications (game, learning
software, etc), which are classified as level, subjects..
• Tailoring the learning system’s interface and content
feature to the cognitive and learning needs and capacities
• Java servlets, HTML, Javascript, Oracle, and JDBC
Tracking Examples (Card Game)
Tracking Examples (Word
Learning)
Tracking Examples (Mouse
Trajectory)
Tracking Examples ( web
browser)
Tracking and Adaptiveness
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Java Classes
Javascripts : event Object from Javascript
Background Process (e.g. NeatTools by Yuh-Jye Chang)
Adaptiveness : Tracked Information is reflected back to the
learning environment.
SD Information Architecture
• Registration and Customization process are
conceptually handled by AS.
• At SD, Servlets play the role as AS in the
above processes between user requests and
a content server.
User Registration
Customization
Servlets at AS
Regist..
AS
Custom..
.
.
DB
Table (User Information)
• Create table sd_table{
userid
ufname
ulname
uschool
usex
umajor
unation
ucomment
uip
varchar2,
varchar2,
varchar2,
varchar2,
varchar2,
varchar2,
varchar2,
varchar2,
varchar2};
Table (Objects’ Information)
• Create table object_table{
ID
varchar2
name
image
url
a_level
field
description
author_name
author_contact
varchar2
varchar2
varchar2
varchar2
varchar2
varchar2
varchar2
varchar2};
SD Event Model
User
SD
Client
Brower
AS
CS
Data and Event flow at SD
Server
user
client
AS
CS
CS
Client-side event
• Javascript : easily added to the “head” of
any html file for “event” detection of
keydown, keyup, mouse move, mouse
location, event time, double click, etc. (Mix
‘n match!)
• With proper browser detection scheme, it
will work at both IE and Netscape.
Client-side event Continued
• NeatTool : “replay” functionality, using
standard MS-windows file type (text file
can be parsed for further analysis)
• Both needs to send information back to
server through web browser or other ways.
• Easy and accurate
• How user interact with the interface.
Event.ntl file for client side event
detection
Javascript Code for client side
event detection
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document.onmousedown=mouse_down;
document.onmouseup=mouse_up;
document.onkeypress=key_up;
document.ondblclick=double_click;
document.captureEvents(Event.KEYPRESS|E
vent.MOUSEDOWN|Event.MOUSEUP|event.DB
LCLICK);
Example of Javascript Function
function key_up(e){
now = new Date;
var keyChar = String.fromCharCode(e.which);
var px = e.pageX;
var py = e.pageY;
var text="<fontcolor=navy>["+now.getHours()+":"+
now.getMinutes()+":"+now.getSeconds()+"]</font
>";}
Server-side event
• Servlets and Perl scripts at AS side,
communicate with database, file system,
and log files.
• User transaction at server side shows time
and data information, not how user
interacted with interface
• Also we can tune the system based on such
data. (idea of the web benchmark)
Events at SD in XML (DTD)
<?XML version=“1.0”?>
<!DOCTYPE DOCUMENT[
<!ELEMENT DOCUMENT
(USER_SESSION) *>
<!ELEMENT USER_SESSION (NAME,
DATE,
C_DATA_TEXT,C_DATA_TRACK,S_DA
TA)>
XML CONTINUED
<!ELEMENT NAME (LNAME, FNAME)>
<!ELEMENT LNAME (#PCDATA)>
<!ELEMENT FNAME (#PCDATA)>
<!ELEMENT DATE (#PCDATA)>
<!ELEMENT C_DATA_TEXT (CLIENT)*>
<!ELEMENT CLIENT (S_TIME,
DATA,WHICH)>
XML CONTINUED
<!ELEMENT STIME (#PCDATA)>
<!ELEMENT DATA (#PCDATA)>
<!ELEMENT WHICH (#PCDATA)>
<!ELEMENT S_DATA (SERVER) *>
<!ELEMENT SERVER (STIME, DATA,
WHICH)>
XML CONTINUED
<!ELEMENT C_DATA_TRACK
(T_FILE?,TITLE)>
<!ELEMENT TITLE (#PCDATA)>
<!ELEMENT T_FILE EMPTY>
<!ATTLIST T_FILE TYPE CDATA
“TEXT/PLAIN”>
Interactivity
• A variable characteristic to describe
communication or interactions between (sub)
systems.
• Interactive learner-centered approach works better
than repetition or drilling (Krashen 1981)
• Jean Piaget : Learning occurs through the
constructive processes of assimilation and
adjustment. (not filling empty container with
information!)
Structure of This Thesis
Measurement of Interactivity at an
Information Architecture for a
Learning Environment
• Education, learning theory (instrumental
learning), and commercial field’s approach
were researched.
• Content-based analysis - attractiveness,
choice, adjustment, information collection,
and off-site contacts.
• User transaction data analysis - client and
server sides
User-transaction record analysis
• Benchmark (quantitative data analysis)
• Performance of web server or arrangement of hyperlinks
affect a user’s navigation patterns.
– Perception cycles, processing of visual information, or reaction
times of the human being
– delay in downloading affect learning too.
• Web benchmark : measure raw throughput and the
handling capacity, performance statistics of web server.
• We want to do the same but focus on user behavior - how
they interact within a learning environment
How we combine to apply two
analyses.
• Step 1. Assume a virtual user trajectory
• Step 2. Build 2 Personalized SDs in include the trajectory
• Step 3. Measure the time and transferred data when a
virtual user follows the trajectory.
• Content-based analysis is presented by a weighted 2 D
graph.
Content-based analysis generated by
a graph generator written in Java
User transaction data analysis
• Variables : time taken at each page, at each link, and data
transferred.
• More links at a page will cause longer “View” time.
• The number of links at each page at the virtual path is
required to calculate the average time spent to look at
links.
• View time = the average time spent at links + time taken to
read contents of the page
• domains of variable: can be various distribution,
exponential, logarithmic, or linear. (learning curve)
Number of Hits vs. Time
• X-axis: time taken to read contents of page
– do not include the average time at each link
• as x=10, SD is 0.027, P-SD1 is 0.037, and P-SD2
is 0.039.
Transferred Data vs. Time (1)
• One second per link is assumed.
• As x=10, SD is 2400 bytes, P-SD1 is 3500 bytes, and PSD2 is 3300 bytes.
• A user takes less time in searching for the path in learning
and makes more data/information requests to a server.
Transferred Data vs. Time (2)
• 1.5 seconds per link is assumed.
• Transferred data per second is less here.
• Perception capability of each user brings
differences in measuring interactivity
Total Completion Time
• Same tangent of 17.5
A learning curve Y = 100 * ex
• 100 is an initial rate.
• As the value of x moves 2 to 3, Y value is
738.9 to 2,008.6.
Adaptiveness issues
• 58.4 % increase in transferred data if we
decide to add more subjects to the learner’s
environment.
Conclusion
• A generic web based information architecture for a
learning environment has been proposed
throughout this paper.
• Adaptive control system and discrete event system
are used to capture the idea of learning process.
• Information Architecture
– is proposed to serve required Services for our
learning environment at the Internet.
Conclusion Continued
• Events : any form of operational or data
requests/responses among subsystems.
• Events at the “interaction machine” allows
us to have event model with interaction
model paradigm.
• Interactive agents have greater questionanswering ability than Turing machine.
Conclusion Continued
• Interactivity at the information architecture
is explored.
• From the content-based analysis, we saw
how practical “requirements” were reflected
and considered in designing a system, e.g.,
educational requirement were taken care of.
• With the client side event detection, we got
detailed natures of user-generated events.
Conclusion Continued
• Server side interaction showed us
quantitative data to examine the nature of
interaction so we can compare systems or
improve the system.
• Here we could tell interactive and adaptive
system worked better.
Conclusion Continued
• Proving correctness at an interaction model
is to show that components have collection
of interfaces corresponding to desired forms
of useful behavior. (Wegner 1997)
Conclusion Continued
• We provided foundation to look at
properties of interactions among
components.
• This empirical ways of measuring
interaction based on events will make
interaction model richer.
Future Work
• Modeling and simulation in a real time
learning environment
• “event queues” in abstracting the system
will enable the addition of concepts of
sharing events or
asynchronous/synchronous events into an
information architecture
Question?
Thanks to...
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Dr. Geoffrey Fox - My adviser
Dr. Dave Warner
Dr. Edward Lipson
Matt Carbone
Taviare Hawkins
Rahul Panesar
Yuh-Jye Chang