05 distance learning standards-scorm research

Distance Learning
Standards – SCORM
(Research)
Timothy K. Shih

Outline
• Ubiquitous Learning with SCORM
• MINE Authoring Tools
• MINE LMSs
• Summary

Ubiquitous e-Learning Devices
PDAs Phones
Hyper Pen and Book
PCs
Digital TV and
Set-top Box

MINE SCORM Authoring
Tools
A Partner of the Academic ADL Co-Lab

Design Issues
• Authoring Hard SCORM Tags
• Video Presentation and Flash Playback
• The Metadata Wizard
• Automatic Sequencing Testing
• Pre-fetching of Learning Objects
• Video SCORM

Authoring Hard SCORM
Tags
1. Function
ICONs
2. Content
Aggregation
3. Resource Pool
4. Hard SCORM
Tags
5. Windows for
Designing
Asset
Layout, Metad
ata, and
1
2
3
4
5

Hard SCORM Tags
• Navigation Tags – for Navigation
• Reference Tags – for Multimedia
References
• Answer Tags – for Exams
• Auxiliary Tags – Turn on/off or Control
Hard SCORM LMS

Reference Tags
• Multimedia
References
– Video
– Audio
– Flash
– URL
• Copy Resources
in the New
Content
Aggregation and
PIF

Reading SCORM Courseware
on Hardcopy Books

Searching on Repository
• Local Search
• Server Search
• Repository Search
Search by
Metadata
Add to
Resource Pool

Printing Hardcopy Textbooks
• Reference
Tags are
Embedded in
between
Words
• Different
Navigation
Tags are
Generated for
Different
Sequencing
Specifications

Hardcopy Textbooks
• ID, Control
Panel, Table of
Contents and Index

Hardcopy Textbooks
• Content and Test

Video Presentation and Flash
Playback
• Video and Slide Synchronization
• Recording, Editing, Post Processing, and
Delivery
• Add Metadata to Video and Each Slide
• Support Flash Playback
Video Stream
Text
User Interrupt User Interrupt User Interrupt
Slide Slide Slide

Presentation Recording
• Select
PowerPoint
file
• Adjust
Camera
• Select Style
File
• Start
Recording

Presentation Editing and
Delivery
• Add/Delete Slides
• Combine
Presentations
• Authoring Metadata
• Playback and
Delivery

Alternative Post Process
• Adding Slides and Flash Objects

The Metadata Wizard
• Metadata is useful for object reuse
• Time consuming to fill in metadata
• Background of Users
• Need tools to help the user
– A user profile is filled only once
– Interactive questions may or may not be asked by
the authoring tool
– Deduction rules can be designed by professionals
and customized to individual needs
– The author makes a final confirmation of metadata

Examples of Metadata
Generation
• Environment and Platform Dependent
– 1.3 Language, 2.3.3 Date, 4.1 Format, etc.
• User Profile (provided at login time)
– 2.3.1 Role, 2.3.2 Entity, etc.
• Deduced by Interactive Questions
– Who are the target readers? 
5.6 Context, 5.7 Typical Age Range
• Deduced by Structural Relations
– 5.9 Typical Learning Time, 4.2 Size, etc.

Copy+ and If-Then Rules
• Copy+: via information retrieval techniques
• If-Then Rules: User Defined (by educational
professionals)
– If 5.2 Learning Resource Type = diagram| figure| graph| table
Then 1.8 Aggregation Level = 1；
– If 5.3 Interactivity Level = very low| low| medium Then 5.4
Semantic Density=high | very high；
– If 5.3 Interactivity Level = very high| high Then = 5.1
Interactivity Type =active；
– If 5.3 Interactivity Level = very low | low Then = 5.1
Interactivity Type =expositive；
• Subjective Rules (optional and user dependent)
• If no rule is used, the user need to provide metadata

System Architecture of the
Wizard
Learning Content
Management System
Metadata Wizard
Deduction Engine
Authoring Tool
Metadata Editor
Environment and Platform Information
Deduction
Rules
User
Profile
Question
Agent

Implementation of the
Wizard
• Users make a final
confirmation to use the
generated metadata

Sequencing Testing
• Sequencing Control
Modes
– Choice, Choice
Exit, Flow, Forward Only
• Problems
– “Unreachable” Clusters
– “Sink” Clusters
• Solution
– Automatic Sequencing
Testing
• Integrated with the Hard
SCORM Authoring Tool

Example Activity Tree – A
Problem
Cluster 1
Cluster 2 Cluster 6 Cluster 7
Cluster 10leaf leafCluster 3 leaf Cluster 5 Cluster 8
Cluster 9
leaf
leafleaf
leaf
Cluster 4leaf leafleaf
leafleaf
leaf
Choice = true
Choice Exit = true
Flow = false
Forward Only = false
Choice = true
Choice Exit = true
Flow = false
Choice = true
Choice Exit = true
Flow = false
Choice = true
Choice Exit = true
Flow = false
Choice = true
Choice Exit = true
Flow = false
Sink
Unreachable

Decompose the Problem
Module
Leaf
Module
LeafLeaf Leaf
Choice = true
Choice Exit = true
Flow = false
Choice = true
Choice Exit = true
Flow = false
Choice = true
Choice Exit = true
Flow = false
Choice = true
Choice Exit = true
Flow = false
Choice = true
Choice Exit = true
Flow = false
Choice = true
Choice Exit = true
Flow = false

Analysis via Truth Table Module
LeafAll Cases

Deduced Truth Table (partial)
Parent Activity (Module) Child Activity (Leaf)
Flow Forward
Only
Choice Choice Exit
True False x x
True True False x
True True True x
True True True x
False True False x
False True True x
False True True x
False False False x
False False True x
False False True x
Flow Forward
Only
Choice Choice
Exit
Result
x x x x ok
x x x x ok
x x x True ok
x x x False blocking
x x x x blocking
x x x True ok
x x x x blocking
x x x True ok
x : Don’t Care

Example 1
Parent Activity (Module) Child Activity
(Leaf)
Choice Exit Result
False blocking
Module
Lesson 2Lesson 1 Lesson 3 Lesson 4
Flow Forward Only Choice
False True True
Flow = false
Forward Only = true
Choice = true
Choice Exit = true
Flow = false
Choice = true
Choice Exit = true
Flow = false
Choice = true
Choice Exit = false
Flow = false
Choice = true
Choice Exit = true
Flow = false
Choice = true
Choice Exit = true
Activity
Sink Activity

Example 2
Module
Lesson 2Lesson 1 Lesson 3 Lesson 4
Flow = false
Choice = false
Choice Exit = true
Parent Activity (Module) Child Activity
(Leaf)
Choice Exit Result
X blocking
Flow Forward Only Choice
False False False
Activity
Unreachable Activity

Bottom up Testing
Cluster 1
Cluster 2 Cluster 6 Cluster 7
Cluster 10leaf leafCluster 3 leaf Cluster 5 Cluster 8
Cluster 9
leaf
leafleaf
leaf
Cluster 4leaf leafleaf
leafleaf
leaf

Summary
• Only works for the 4 Basic Control Modes
• Extend for conditions and rollup rules
(and others)
• Automatic Testing
– When to trigger the testing?
• Convenience – User Friendly
– How to fix the bug?
• Proper suggestions by the system
• Metrics of Sequencing and Navigation is
an open issue

Pre-fetching Learning Objects
• Sizes of
Learning
Objects are
Computed
• A Content
Aggregation
is Divided
into Clusters
• Pre-fetching
on PDAs
and Smart
Phones
• An intelligent caching policy is
designed based on
sequencing and navigation
definitions

The Video SCORM Authoring Tool
• Integrated with the Hard SCORM Authoring Tool

URL References
• The Authoring Tool is available at
– http://member.mine.tku.edu.tw/www/fatty/MINE/Hard%20SCORM%20Authorin
g%20Tool%20v%201.0.rar
– http://www.mine.tku.edu.tw/scorm (video demos)
– Implementing the SCORM Forum
• Need .net framework 1.1
• Automatic installation
• User’s manual

Demonstration of Authoring Tool
Video Clip Available at http://www.mine.tku.edu.tw/SCORM/

Conclusions and Suggestions
• S&N is complicate
– Needs Visualization Tool
• Needs S&N Testing Tools
• Needs Metadata Generation
• Need an Open Interface to Repository
– Standard Representation of Search
Specification
– Interface to Federated Repository

MINE SCORM LMSs

Devices Supported by the
LMS
• PC
• Hardcopy Textbook with Hyper-Pen
• PDAs
• Cellular Phones
• TV

Reading via Hyper Pen
• Using Hyper Pen as Input Device
• Reading on Hardcopy Textbooks
• Using Audio Messages for Navigation Control
• Using Multimedia Clips as References

Hard SCORM Machine
(HSM)
• HSM
– based on the concept of a finite state
machine (deterministic finite state machine)
– a finite state machine, M, is represented as
a 5-tuple: ),,,,( 0
FqQM 
finite set of internal
states
input alphabet
a set of Hard SCORM tags
transition function
a set of final states
F = {f}
initial state
q0=i
Qq 0
QF 

States in HSM
• Reading:
While an user is reading in a correct range of reading pages, the
machine is waiting for a tag to be accessed.
• Behavior and Context (BC) Analysis:
Between a tag is used and a correct destination page is
confirmed, the machine stays in the BC Analysis state for action
analysis. This state is used in two-phase transactions.
• Warning:
While a reader is reading in a wrong page (due to an incomplete
two-phase transaction), the Warning state will signal audio
messages and waiting for the reader to provide a correct navigation.
• Suspending:
The reader may suspend the state machine. Counting of learning
time is also suspended.
• Quiz Submachine:
The submachine is implemented as an assessment system. The
system is controlled by ECMA Script of an SCO.

Hard SCORM Machine
Reading Warning
Suspending
BC Analysis
Navigation Tags
Correct
Behavior
Incorrect
Behavior
Navigation Tags
Pause
Continue
Start
End
Reference Tags
Learner Status
Quiz
Start Quiz
End Quiz
Incorrect
Behavior
Correct
Behavior
i
f

The Quiz Submachine
Start
Quiz
End
Quiz
Question
Identification
Answer
Recording
Question Tag
All
Acceptable
Answer
Tags
Question
Tag

Two-phase Transaction
• For Content Navigation
– including page index tags and previous/next page
tags, and committed by a page tag.
– controlled by the Behavior and Context Analysis State
– different sound signal will be used
• For Quiz
– When a Start Quiz Tag is triggered, the submachine
is waiting for a Question tag to identify which question
an answer will be given
– The second phase checks for a correct answer type
associated with its answer value
– controlled by another two-phase transaction, which
takes a Start Quiz tag and an End Quiz Tag

One-phase Transaction
• For the Reference Tags and Auxiliary
Tags
– Reference Tag: Video, Audio…..
– Auxiliary Tags: Pause, Status, Continue……

Transition Table of the Quiz
Submachine

Audio Messages – for
Navigation Control

The Hard SCORM LMS
• Using Web Service with Different Devices
• Encapsulate SCORM APIs by Using Web Service Technology
Learning Management System (LMS)
HTTP Protocol or SOAP Protocol
Server Side
Client Side
Assets
API
Instance
Content
Repository
Launch
SCO
Assets
Imsmanifest
ECMA
Script
LMS
Server
SCORM
Web
Services
WSG
Sequencing Cache Engine
Pocket PC Smart Phone Hyper Pen Browser
Other Devices

Revised ECMA Script for Web
Service
• Extend ECMA Script to Cope with off-line Learning Model
• Use Service Queue and Result Queue
• Use SOAP to Encapsulate Messages

• Integrated with Pocket SCORM Reader
• Consistent Learner Records on PC, PDA, Smart
Phone, TV, and Hyper Pen
Reading on PCs

Demonstration of Hard SCORM
LMS

Demonstration of Hard SCORM
LMS on PC

Pocket SCORM
LinearKnowledge Paced

Auto Re-Flow and Personalized
Notes
Before Re-Flow After Re-Flow Adding Notes

Reading on Smart Phone
Login Register Unpack Select

Caching on Mobile Devices
• SCORM LMSs with Mobile Devices
– Pocket PC
– Smart phone
• Limitation of Storage
• Divide a Course into Several Parts
(Clusters)
• Preserve the Features of Sequencing
• Caching Strategies
– Download Order
– Replacement Order

Clusters in an Activity Tree
Cluster 2 Cluster 3 Cluster 4 Cluster 5
Sequencing Control M ode: Flow = true; Choice = false;
Rollup Rules: Com pleted if all com pleted; Satisfied if all satisfied; Not Satisfied if any Not Satisfied;
Exit Rules: Exit if com pleted
O bjective Satisfied by M easure = true;
O bjective M inim um Satisfied Norm alized M easure = 0.6;
Rollup Rules: Com pleted if all attem pted
Rollup Controls: Rollup O bjective Satisfied = false
Rollup Controls: Rollup O bjective Satisfied = false
M odule 2:
Enhancing Im ages
M odule 3:
Blending Im ages
M odule 1:
Basics
Lesson 1:
Interface
Lesson 9:
Transform
Lesson 8:
Selection Tools
Lesson 7:
Hue/Saturation
Lesson 6:
Brightness/Contrast
Lesson 5:
Color Balance
Lesson 4:
Layers
Lesson 3:
Palettes
Lesson 2:
Toolbox
Exam
(Assessm ent)
Introduction
Photoshop Exam ple -- Linear
Q uestion 1
Q uestion 3
Q uestion 2
Q uestion 4
Q uestion 7
Q uestion 6
Q uestion 5
Q uestion 8
Q uestion 9
Cluster1

Cluster Download Order
If Control Mode = Flow or Forward-Only Then
Download by Cluster Order (in content aggregation)
(bread first search approach)
If Control Mode = Choice or Choice-Exit Then
Apply Max Fit Strategy to Clusters
(smaller cluster has a higher priority)
(try to load maximum number of clusters)
• Recursive Strategy to Decompose an
Activity Tree
• Order Decision Strategy

Cluster 1
Leaf
LeafLeaf
LeafLeaf
Leaf
Leaf Leaf
Leaf Leaf
Leaf
Leaf
Leaf
Cluster 2
Cluster 3
Cluster 4
Cluster 5
Cluster 6 Cluster 7
Cluster 8
Cluster 9
Cluster 10
Sequencing Control Choice = True
Sequencing Control Flow = False
Sequencing Control Choice = False
Sequencing Control Flow = True
Sequencing Control Choice = False
Sequencing Control Flow = True
Download Order: 1, 7, 2, 6, 3, 4, 5, 8, 10, 9
Sequencing Control Choice = True
Sequencing Control Flow = False
NL: Number of Leafs (representing sizes)
NL=0
NL=2
NL=2NL=1 NL=0
NL=1 NL=2
NL=2
NL=1
NL=2

Cluster Replacement Order
Given a Target Cluster (TC) to be replaced
set RBS = 0
While RBS <= α{ /* make space available */
C = Max-Distance(TC, Clusters)
RBS = RBS + size(C)
Release Cluster C
}
Let L = Download Order of Activity Tree
While RBS > 0 { /* reuse the space */
Load the 1st Cluster C in L
Remove C from L
RBS = RBS - size(C)
}
• Distance Factors
between Two
Clusters
– RC: Reference
Count
– LDT: Last Download
Time
– LAT: Last Access
Time
– PL: Path Length (in
the activity tree)
– CN: Cluster Number
– CS: Cluster Size
• α: Buffer Releasing
Threshold
• RBS: Released
Buffer Size

Connectivity
• Dynamic Replacement
– Used when interaction is low
– Can be turn on/off by the users
• Off-line Mode
– Store navigation messages
– Automatic update when connected

PDAs and Smart Phones
Supported
• Running on PDAs
– Dopod 700, HP iPAQ 5550, and AnexTEK
SP230
• Running on Cellular Phones
– Dopod 565 and Mio8390
AnexTEK SP230Dopod 700 HP iPAQ 5550
Dopod 565 Mio 8390

Demonstration of Pocket
SCORM LMS on PDA

Demonstration of Pocket
SCORM LMS on Smart Phone

Reference URLs
• Demonstration by Trans Asia Airline
– Pocket SCORM: the 2005 Brandon Hall Excellence in Learning
Awards, Innovative Technology
– http://www.elearn.org.tw/PocketSCORM/
• Other Demonstrations
– http://www.mine.tku.edu.tw/scorm
• Implementing the SCORM Forum

• Hyper Video
• Interactive Lecture
• Video Annotation
– Picture
– Text
– WWW
• Interactive Player
• Interactive Video Authoring Tool
• Annotate MPEG-2 (User Defined Data)
Interactive
Video

Multistory Video
• User Interaction (i.e., hyper jump)
• User Annotation (i.e., picture, text, URL, etc.)
Start point End point
Sequence start point Sequence end point
Text
Annotation
Picture
Annotation

Interactive Video Authoring
Tool
Story Board
Video
Editing
Window
Annotation
Editing
Functions
Video
Sequences

Interactive Video Player
Annotation
and
Reference
Video
Playback
and Hyper
Tags
Video Control Panel
Hyper Jump TagAnnotation Tag Web Reference
Web Control Panel

Demo of Interactive Video

Interactive Video Using Gesture and
Remote Control

• How about SCORM on TV?
• How about interactivity?
• How about standard?
• The DVB Multimedia Home Platform
(MHP)
– Defined by the DVB consortium
– Adopted in many countries
• Italy, Germany, Finland, Singapore, S. Korea, Australia and others
• Included in the US OpenCable & ACAP standards
• Can we combine MHP with SCORM?
The Video SCORM Project

What is MHP
• A Platform Definition
• A Set of Java APIs
• A Set of HTML Document Type Definitions
• An Extension to Existing Open Standards DVB,
MPEG, JavaTV
• MHP 1.0.x (1.0.0 – 1.0.3)
– The original MHP specification plus updates
– The most commonly deployed version of MHP
• MHP 1.1.x
– HTML Support, Stored Applications, Internet Client
APIs, Smart Card APIs

The Video SCORM Authoring
Tool
• Integrated with the Hard SCORM Authoring Tool
Video Tag
Web Tag
Content
Aggregation
Properties
Scene

The Video SCORM Authoring
Tool
• A video SCORM component is an SCO
• Divides scenes into video stream files
• Allow users to add metadata and
sequence rules
• However, sequence rules among
scenes is our future work
Scene
Actor for video jump
Web link

The Video SCORM Run-time
• Integrated with the Hard SCORM LMS (Web-based)
• Download video SCORM components (SCOs)
Video Scene
Web Content
Video Control

Demonstration of Video
SCORM

Interactive Digital TV
• Traditional Cable TV
• Interactive TV (Video): interactivity
between the users and broadcasting
program, could be PC-based
• Set-top Box and Digital TV
– Limited computation power
– Limited input device
• Is MHP a solution?

Running an MHP-based
Program
• You must have:
– Transport stream and object carousel
generator
– A playback system
– Cable or satellite TV channel
• Expensive? Yes
• Alternative resource for research
– Digital TV Simulator

TV SCORM on a Simulator
• Simulator: OSMOSYS SDK 2.1 (MHP 1.1)
• Integrated with our SCORM LMS

TV SCORM on a Simulator
• Aggregation Tree (on/off)
• Remote Controller for Navigation

Demonstration of TV SCORM

General Broadcasting System
Multiplexer
MPEG-2
encoder
MPEG-2
encoder
Object carousel generator
(broadcast file system)
Video
capture
tool
Audio
capture
tool
Content
Authoring &
preparation
Modulator
Upconverter
Receiver (Set Top Box)
MPEG-2
Elementary stream
Full transport stream
(Incl. service information )
Transport stream playout system
For satellite network only
Raw video & audio data Directories containing
applications & assets

Status of Video SCORM
Project
• PC-based Interactive Video
– Video SCORM Authoring Tool
– Video SCORM Run-time Environment
– Integrated with the Hard SCORM LMS
• Set-top Box and Broadcasting System
– TV SCORM on Simulator
• Read SCORM-based contents
– Integrated with the Hard SCORM LMS
– Interactive Video is not fully implemented

Conclusions and Suggestions
• Web Service and Centralized Delivery
• Java-Based LMS
• Need detailed definition of learner
records
– Activity Tree, Student Records, Transcripts
• What about Web 2.0?
• What about Grid?
– Flexible Delivery Paths
– Flexible Computation Services

Status and Open Issues
• Status of CORDRA
• Repository for questions and tests – Q&TI?
• Need representation of learner profile – activity
tree, student performance, transcripts
• Intelligent Tutoring – based on assessment
outcome and S&N rules
• Simulation and Games
• The Integration of Ubiquitous Computing and
Grid Computing

Acknowledgement
• Judy Brown, Director of Academic ADL
Co-Lab
• David Wirth, Deputy Director of Academic
ADL Co-Lab
• John Toews, Academic ADL Co-Lab
• Doug Hamilton, Academic ADL Co-Lab
We will like to thank the following people for their
discussion and suggestions:

Thank You
E-Learning Team, MINE Lab, Tamkang University
Advisors and Doctors Ph. D. Candidates
MS Graduates and MS Students

05 distance learning standards-scorm research

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