Information Architecture of Information Architecture of Interactive and Customizable Interactive and Customizable

Learning EnvironmentsLearning 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

HCI

EducationLearning

Environment

Human factorengineering

ComputerScience

Cognitive Psychology

Cybernetics

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

• 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

• 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

• 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• 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

• 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

• 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

AS

Regist..

Custom..

..

DB

Table (User Information)

• Create table sd_table{

userid varchar2,

ufname varchar2,

ulnamevarchar2,

uschool varchar2,

usex varchar2,

umajor varchar2,

unation varchar2,

ucomment varchar2,

uip varchar2};

Table (Objects’ Information)

• Create table object_table{

ID varchar2

name varchar2

image varchar2

url varchar2

a_level varchar2

field varchar2

description varchar2

author_name varchar2

author_contactvarchar2};

SD Event Model

User

SDClient

Brower AS CS

Data and Event flow at SD

Server

user client CSAS 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

• document.onmousedown=mouse_down;

• document.onmouseup=mouse_up;

• document.onkeypress=key_up;

• document.ondblclick=double_click;• document.captureEvents(Event.KEYPRESS|

Event.MOUSEDOWN|Event.MOUSEUP|event.DBLCLICK);

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_DATA)>

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 P-SD2 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 question-answering 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...

• Dr. Geoffrey Fox - My adviser• Dr. Dave Warner • Dr. Edward Lipson• Matt Carbone• Taviare Hawkins• Rahul Panesar• Yuh-Jye Chang