1. Pedro Salcedo Lagos psalcedo@udec.cl
MISTRAL: A Knowledge-Based System for Distance Education that Incorporates Neural Networks Techniques for Teaching Decisions
Pedro Salcedo Lagos
Universidad de Concepción, Chile

2. Nobel Prize laureate in Literature (Chile), 1945
Gabriela Mistral
Nobel Prize laureate in Literature (Chile), 1945

3. This is the place
where I come from

4. Knowledge Based System
Motivation
Neural
Networks
MISTRAL
Knowledge Based System
Internet
Distance
Education

5. Contents Current Platforms (Overview) The Platform MISTRAL
NNs in MISTRAL

6. Some revised Platforms
The Learning Manager
WebMentor
Alfa Net - UNED
IVLE
LUVIT
Asymetrix Librarian
Virtual-U
eduprise.com
Blackboard
Convene
Embanet
eCollege.com
IntraLearn
Symposium
TopClass
WebCT

7. Tools for Instructors Course planning On-line evaluation
Tools for Instructors
Course planning
Most of platforms
On-line evaluation
Some of platforms
Specific material addressing
A few platforms

8. Instruction Advantages
Consistent Interface
Most of platforms
Synchronic communication
Support internal
Some platforms
Help on-line
A few platforms

9. Tools for Students Multimedia support Discussion groups
Most of platforms
Discussion groups
Anouncement panel
Some platforms
Progress tracking
A few platforms

10. And Costs... Some are free (AulaWeb)
Some are expensive (about US $ 90,000 for Docent)
Some have a cost per year (about US $ 5,000 for Course Info)
Some include maintenance costs..., creation costs...

11. Most of them considering 5 tasks
Material preparation
Teaching-learning strategies generation
Dialog capability
Evaluation
Management

12. Material Preparation Current Proposed by MISTRAL Time consuming
Use activities instead of contents
Examples:
Read chapter X in text Y
Use simulation tools
Participate in a discussion group

13. Strategies Generation
Current
Found in only one commercial platform (Cameleon)
Proposed by MISTRAL
Adapts to student profile considering learning styles and previous knowledge
Risk students’ detection

14. Dialog Capability Current Proposed by MISTRAL
Exists in most of platform
Without using AI techniques
Proposed by MISTRAL
Adapts media to user profiles using AI rules
Automatic detection of interaction degree
Personalized activities (via , portfolio)

15. Evaluation Current Proposed by MISTRAL Low level of development
Assumed by the instructor
Proposed by MISTRAL
Adaptive evaluation based on user modelling
use of Bayes nets
Portfolio techniques
use of virtual folders

16. Management Current Proposed by MISTRAL Exists in all revised platforms
Lack of support for instructor or manager
Proposed by MISTRAL
Automatic statistics generation
general course behavior
interactivity level
...
via

17. MISTRAL Adapts to students’ profile by suggesting
Activities to be accomplished
Tools to be used
Evaluation mechanisms
for correcting detected problems

18. MISTRAL (cont) Multiuser modality Easy knowledge acquisition
contents and activities for each course
Folder structure
based in portfolio strategy
Diagnostic and detection of knowledge level
using rules and Bayes nets

19. MISTRAL (cont) Virtual room personalized profile group interaction
warning messages
when participation level of students is lower than expected

20. Neural Networks in MISTRAL
Rules
Objects
Specified knowledge
Corrective actions generation
when detecting possible unsuccessful performance

21. Sequence of activities in MISTRAL for NN use
yes
Get student
Trained net
Information no
Detect Failure
Net training
Risk no
Failure
Probabil.
yes
Determine
corrective
actions

22. Application development
The neural net has one hidden layer and number of nodes is variable depending on number of factors to be considered
The activation function is hyperbolic tangent

23. If Neural Network option is used

24. There are three additional entries

25. Following with variables configuration

26. Training the net

27. Visualizing learning parameters

28. Net training
During training, the net learns to classify students in successful group or failure risk group depending on different psicosocial characteristics upoin requirements
Data can be classified as default data (provided by MISTRAL) or user provided data

29. Implementing

30. Technical considerations
Windows 2000 server (translating to Linux)
Database access through ODBC using SQL (translating to MySQL)
Programming in VBScript, JavaScript and Java with ASP (translating to PHP)
Bayes net has been programmed using JavaBayes library

32. Conclusions MISTRAL Platform allows
increase productivity both in organizations oriented to distance education and in instructor work
improvement in distance courses management
maintain intellectual property
get a better distance education teaching/learning process, really personalized and efficient

33. Current work
Search for more adequate threshold functions, learning rules and NN architecture to determine students’ success
Learning coefficient and momentum revision, more adequate to Generalized Delta Rule
Net training to adapt learning strategies to different contexts
Different courses knowledge acquisition

34. Thanks for your time...
Pedro Salcedo Lagos