This document discusses standards for distance learning known as SCORM (Sharable Content Object Reference Model). It outlines the MINE authoring tools and learning management systems that were developed as part of this research to support the SCORM standard. The tools discussed include an authoring tool for creating SCORM compliant courses, a metadata wizard to help authors populate metadata fields, and sequencing testing to check for issues in course navigation. Support for mobile and ubiquitous learning is also discussed through features like pre-fetching of content and alternate formats like video SCORM.
This is an intermediate conversion course for C++, suitable for second year computing students who may have learned Java or another language in first year.
A Combined Method for E-Learning Ontology Population based on NLP and User Ac...Fred Kozlov
The paper describes a combined approach to maintaining an E-Learning ontology in dynamic and changing educational environment. The developed NLP algorithm based on morpho-syntactic patterns is applied for terminology extraction from course tasks that allows to interlink extracted terms with the instances of the system’s ontology whenever some educational materials are changed. These links are used to gather statistics, evaluate quality of lectures' and tasks’ materials, analyse students’ answers to the tasks and detect difficult terminology of the course in general (for the teachers) and its understandability in particular (for every student).
The Cambridge Marketing Book Club has been created as a forum for marketers and business people to meet in a friendly and informal context, and to meet with authors of both newly published and well-known business books. These sessions are designed to be highly engaging, and to deliver ongoing learning and skills development.
This Guide has been written specifically to assist marketers who are involved in both studying and implementing digital marketing. It includes examples and activities to help reinforce your learning, and recommended reading and website links for additional information. We recommend that you work through the Guide from beginning to end undertaking the exercises and supplementary reading included.
This is an intermediate conversion course for C++, suitable for second year computing students who may have learned Java or another language in first year.
A Combined Method for E-Learning Ontology Population based on NLP and User Ac...Fred Kozlov
The paper describes a combined approach to maintaining an E-Learning ontology in dynamic and changing educational environment. The developed NLP algorithm based on morpho-syntactic patterns is applied for terminology extraction from course tasks that allows to interlink extracted terms with the instances of the system’s ontology whenever some educational materials are changed. These links are used to gather statistics, evaluate quality of lectures' and tasks’ materials, analyse students’ answers to the tasks and detect difficult terminology of the course in general (for the teachers) and its understandability in particular (for every student).
The Cambridge Marketing Book Club has been created as a forum for marketers and business people to meet in a friendly and informal context, and to meet with authors of both newly published and well-known business books. These sessions are designed to be highly engaging, and to deliver ongoing learning and skills development.
This Guide has been written specifically to assist marketers who are involved in both studying and implementing digital marketing. It includes examples and activities to help reinforce your learning, and recommended reading and website links for additional information. We recommend that you work through the Guide from beginning to end undertaking the exercises and supplementary reading included.
Rapid pruning of search space through hierarchical matchinglucenerevolution
Presented by Chandra Mouleeswaran, Co Chair at Intellifest.org, ThreatMetrix
This talk will present our experiences in using Lucene/Solr to the classification of user and device data. On a daily basis, ThreatMetrix, Inc., handles a huge volume of volatile data. The primary challenge is rapidly and precisely classifying each incoming transaction, by searching a huge index within a very strict latency specification. The audience will be taken through the various design choices and the lessons learned. Details on introducing a hierarchical search procedure that systematically divides the search space into manageable partitions, yet maintaining precision, will be presented.
Apache con big data 2015 - Data Science from the trenchesVinay Shukla
ApacheBigData - Budapest, 2015
Data Science from the trenches
What are the issues?
How to select best algorithm?
How to tune?
What are the problems with visualization?
How does Zeppelin help
Performance Issue? Machine Learning to the rescue!Maarten Smeets
t can be difficult to determine how to improve performance of microservices. There are many factors you can vary but which factor will be the one having most impact? During this presentation, a method using the random forest machine learning algorithm will be applied in order to help improve performance of a microservice running inside a JVM. Several measures are taken such as thoughput and response times. Java version, JVM supplier, heap, garbage collection algorithm and microservice framework are all varied. Which factor is most important in determining the response time and throughput of the services? The Random Forest algorithm will be introduced to solve this challenge. Not only will this presentation give some useful suggestions for improving the performance of microservices but will also introduce a novel way to take on the challenge of performance tuning which can be applied to other use-cases. This presentation is especially interesting to developers and architects.
Presentazione Tesi Laurea Triennale in InformaticaLuca Marignati
Università degli Studi di Torino
Dipartimento di Informatica
Titolo: Apprendimento per Rinforzo e Applicazione ai Problemi di Pianificazione del Percorso
Topic: Machine Learning
The World Wide Web is moving from a Web of hyper-linked documents to a Web of linked data. Thanks to the Semantic Web technological stack and to the more recent Linked Open Data (LOD) initiative, a vast amount of RDF data have been published in freely accessible datasets connected with each other to form the so called LOD cloud. As of today, we have tons of RDF data available in the Web of Data, but only a few applications really exploit their potential power. The availability of such data is for sure an opportunity to feed personalized information access tools such as recommender systems. We will show how to plug Linked Open Data in a recommendation engine in order to build a new generation of LOD-enabled applications.
(Lecture given @ the 11th Reasoning Web Summer School - Berlin - August 1, 2015)
Building High Available and Scalable Machine Learning ApplicationsYalçın Yenigün
The slide contains some high level information about some machine learning algorithms, cross validation and feature extraction techniques. It also contains high level techniques about high available and scalable ML products.
This presentation talks about Natural Language Processing using Java. At Museaic, a music intelligence platform, we spent time figuring out how to extract central themes from song lyrics. In this talk, I will cover some of the tasks involved in natural language processing such as named entity recognition, word sense disambiguation and concept/theme extraction. I will also cover libraries available in java such as stanford-nlp, dbpedia-spotlight and graph approaches using WordNet and semantic databases. This talk would help people understand text processing beyond simple keyword approaches and provide them with some of the best techniques/libraries for it in the Java world.
An introduction to variable and feature selectionMarco Meoni
Presentation of a great paper from Isabelle Guyon (Clopinet) and André Elisseeff (Max Planck Institute) back in 2003, which outlines the main techniques for feature selection and model validation in machine learning systems
Brand new to Sumo Logic? Get started with these 5 easy steps and get certified!
Learn the basics for how to search, parse and analyze the logs and metrics that are important to your organization. This session will guide you through running searches, simple parsing and basic analytics on your data. Learn how to convert your queries to charts and add them to Dashboards to help you visualize trends and easily identify anomalies. Lastly, learn how Alerts can help you stay on top of your critical events.
Lucene/Solr Revolution 2015: Where Search Meets Machine LearningJoaquin Delgado PhD.
Search engines have focused on solving the document retrieval problem, so their scoring functions do not handle naturally non-traditional IR data types, such as numerical or categorical. Therefore, on domains beyond traditional search, scores representing strengths of associations or matches may vary widely. As such, the original model doesn’t suffice, so relevance ranking is performed as a two-phase approach with 1) regular search 2) external model to re-rank the filtered items. Metrics such as click-through and conversion rates are associated with the users’ response to items served. The predicted selection rates that arise in real-time can be critical for optimal matching. For example, in recommender systems, predicted performance of a recommended item in a given context, also called response prediction, is often used in determining a set of recommendations to serve in relation to a given serving opportunity. Similar techniques are used in the advertising domain. To address this issue the authors have created ML-Scoring, an open source framework that tightly integrates machine learning models into a popular search engine (SOLR/Elasticsearch), replacing the default IR-based ranking function. A custom model is trained through either Weka or Spark and it is loaded as a plugin used at query time to compute custom scores.
Rapid pruning of search space through hierarchical matchinglucenerevolution
Presented by Chandra Mouleeswaran, Co Chair at Intellifest.org, ThreatMetrix
This talk will present our experiences in using Lucene/Solr to the classification of user and device data. On a daily basis, ThreatMetrix, Inc., handles a huge volume of volatile data. The primary challenge is rapidly and precisely classifying each incoming transaction, by searching a huge index within a very strict latency specification. The audience will be taken through the various design choices and the lessons learned. Details on introducing a hierarchical search procedure that systematically divides the search space into manageable partitions, yet maintaining precision, will be presented.
Apache con big data 2015 - Data Science from the trenchesVinay Shukla
ApacheBigData - Budapest, 2015
Data Science from the trenches
What are the issues?
How to select best algorithm?
How to tune?
What are the problems with visualization?
How does Zeppelin help
Performance Issue? Machine Learning to the rescue!Maarten Smeets
t can be difficult to determine how to improve performance of microservices. There are many factors you can vary but which factor will be the one having most impact? During this presentation, a method using the random forest machine learning algorithm will be applied in order to help improve performance of a microservice running inside a JVM. Several measures are taken such as thoughput and response times. Java version, JVM supplier, heap, garbage collection algorithm and microservice framework are all varied. Which factor is most important in determining the response time and throughput of the services? The Random Forest algorithm will be introduced to solve this challenge. Not only will this presentation give some useful suggestions for improving the performance of microservices but will also introduce a novel way to take on the challenge of performance tuning which can be applied to other use-cases. This presentation is especially interesting to developers and architects.
Presentazione Tesi Laurea Triennale in InformaticaLuca Marignati
Università degli Studi di Torino
Dipartimento di Informatica
Titolo: Apprendimento per Rinforzo e Applicazione ai Problemi di Pianificazione del Percorso
Topic: Machine Learning
The World Wide Web is moving from a Web of hyper-linked documents to a Web of linked data. Thanks to the Semantic Web technological stack and to the more recent Linked Open Data (LOD) initiative, a vast amount of RDF data have been published in freely accessible datasets connected with each other to form the so called LOD cloud. As of today, we have tons of RDF data available in the Web of Data, but only a few applications really exploit their potential power. The availability of such data is for sure an opportunity to feed personalized information access tools such as recommender systems. We will show how to plug Linked Open Data in a recommendation engine in order to build a new generation of LOD-enabled applications.
(Lecture given @ the 11th Reasoning Web Summer School - Berlin - August 1, 2015)
Building High Available and Scalable Machine Learning ApplicationsYalçın Yenigün
The slide contains some high level information about some machine learning algorithms, cross validation and feature extraction techniques. It also contains high level techniques about high available and scalable ML products.
This presentation talks about Natural Language Processing using Java. At Museaic, a music intelligence platform, we spent time figuring out how to extract central themes from song lyrics. In this talk, I will cover some of the tasks involved in natural language processing such as named entity recognition, word sense disambiguation and concept/theme extraction. I will also cover libraries available in java such as stanford-nlp, dbpedia-spotlight and graph approaches using WordNet and semantic databases. This talk would help people understand text processing beyond simple keyword approaches and provide them with some of the best techniques/libraries for it in the Java world.
An introduction to variable and feature selectionMarco Meoni
Presentation of a great paper from Isabelle Guyon (Clopinet) and André Elisseeff (Max Planck Institute) back in 2003, which outlines the main techniques for feature selection and model validation in machine learning systems
Brand new to Sumo Logic? Get started with these 5 easy steps and get certified!
Learn the basics for how to search, parse and analyze the logs and metrics that are important to your organization. This session will guide you through running searches, simple parsing and basic analytics on your data. Learn how to convert your queries to charts and add them to Dashboards to help you visualize trends and easily identify anomalies. Lastly, learn how Alerts can help you stay on top of your critical events.
Lucene/Solr Revolution 2015: Where Search Meets Machine LearningJoaquin Delgado PhD.
Search engines have focused on solving the document retrieval problem, so their scoring functions do not handle naturally non-traditional IR data types, such as numerical or categorical. Therefore, on domains beyond traditional search, scores representing strengths of associations or matches may vary widely. As such, the original model doesn’t suffice, so relevance ranking is performed as a two-phase approach with 1) regular search 2) external model to re-rank the filtered items. Metrics such as click-through and conversion rates are associated with the users’ response to items served. The predicted selection rates that arise in real-time can be critical for optimal matching. For example, in recommender systems, predicted performance of a recommended item in a given context, also called response prediction, is often used in determining a set of recommendations to serve in relation to a given serving opportunity. Similar techniques are used in the advertising domain. To address this issue the authors have created ML-Scoring, an open source framework that tightly integrates machine learning models into a popular search engine (SOLR/Elasticsearch), replacing the default IR-based ranking function. A custom model is trained through either Weka or Spark and it is loaded as a plugin used at query time to compute custom scores.
Similar to 05 distance learning standards-scorm research (20)
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
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Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
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In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
7. Design Issues
• Authoring Hard SCORM Tags
• Video Presentation and Flash Playback
• The Metadata Wizard
• Automatic Sequencing Testing
• Pre-fetching of Learning Objects
• Video SCORM
8. 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
9. 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
12. Searching on Repository
• Local Search
• Server Search
• Repository Search
Search by
Metadata
Add to
Resource Pool
13. Printing Hardcopy Textbooks
• Reference
Tags are
Embedded in
between
Words
• Different
Navigation
Tags are
Generated for
Different
Sequencing
Specifications
16. 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
20. 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
21. 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.
22. 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
23. 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
29. 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 False blocking
x x x x blocking
x x x True ok
x x x False blocking
x : Don’t Care
33. 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
34. 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
35. The Video SCORM Authoring Tool
• Integrated with the Hard SCORM Authoring Tool
36. 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
38. 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
40. Devices Supported by the
LMS
• PC
• Hardcopy Textbook with Hyper-Pen
• PDAs
• Cellular Phones
• TV
41. 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
42. 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
43. 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.
44. 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
46. 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
47. One-phase Transaction
• For the Reference Tags and Auxiliary
Tags
– Reference Tag: Video, Audio…..
– Auxiliary Tags: Pause, Status, Continue……
51. 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
52. 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
53. • Integrated with Pocket SCORM Reader
• Consistent Learner Records on PC, PDA, Smart
Phone, TV, and Hyper Pen
Reading on PCs
54. Demonstration of Hard SCORM
LMS
Video Clip Available at http://www.mine.tku.edu.tw/SCORM/
55. Demonstration of Hard SCORM
LMS on PC
Video Clip Available at http://www.mine.tku.edu.tw/SCORM/
59. 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
60. 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
Sequencing Control M ode: Flow = true; Choice = false;
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
Sequencing Control M ode: Flow = true; Choice = false;
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
61. 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
62. 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
63. 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
64. 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
65. 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
69. 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
70. • Hyper Video
• Interactive Lecture
• Video Annotation
– Picture
– Text
– WWW
• Interactive Player
• Interactive Video Authoring Tool
• Annotate MPEG-2 (User Defined Data)
Interactive
Video
71. 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
76. • 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
77. 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
78. The Video SCORM Authoring
Tool
• Integrated with the Hard SCORM Authoring Tool
Video Tag
Web Tag
Content
Aggregation
Properties
Scene
79. 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
80. The Video SCORM Run-time
• Integrated with the Hard SCORM LMS (Web-based)
• Download video SCORM components (SCOs)
Video Scene
Web Content
Video Control
82. 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?
83. 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
84. TV SCORM on a Simulator
• Simulator: OSMOSYS SDK 2.1 (MHP 1.1)
• Integrated with our SCORM LMS
85. TV SCORM on a Simulator
• Aggregation Tree (on/off)
• Remote Controller for Navigation
86. Demonstration of TV SCORM
Video Clip Available at http://www.mine.tku.edu.tw/SCORM/
87. 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
88. 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
89. 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
91. 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
92. 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:
93. Thank You
E-Learning Team, MINE Lab, Tamkang University
A Partner of the Academic ADL Co-Lab
Advisors and Doctors Ph. D. Candidates
MS Graduates and MS Students