1. Implementing fuzzy analysis to define a set of training methods for wide range of students
Danil Dintsis
Ph. D.

2. Brief about the presenter
Ph. D. in System Analysis (Doctorate degree, ISCED verified)
Ph.D. in Technical management (Candidate degree, ISCED verified)
Human – machine interaction specialist
Portfolio manager and IT consultant, teacher and coach for 15+ years
Brief about the presenter

3. What is the goal of the presentation
Deliver a brief on my experience in defining and launching training methods by implementing fuzzy analysis
Find colleagues with common points of common professional interest
Improve my scientific and professional experience
Deliver projects and trainings as “guest lecturer”
VU “guest professor/lecturer” program
Impressive results of the Institute of Mathematics and Informatics and the Informatics Methodology dept.
Wide range of successful international high education programs in Lithuania and VU
What is the goal of the presentation

4. life Long education in the information society
Portfolio lead:
Картинки вылетают последовательно
life Long education in the information society

5. Challenges of a lifelong education
Learn and work
Brief training programs – learn only “what I need here and now”
Repeat training material after the training ends
Individual training tempo from highly intensive 3-5 all day long studies to one class per week.
Decrease travel and time expenses
A trainer “is my lead”, a student needs his/her advices and motivation actions не получать избыточных сведений («дайте мне только то, что мне нужно»)
Different demands for visual/audio/text training materials
Labs, practices and cases are the MUST items of the learning process
Prestigious certificate for career development
Challenges of a lifelong education

6. Idea: analyze lingual demands with fuzzy sets
j*a(i), if an object exists only in a formal model
a(i)= a(i), if an object compares to a modelling (1)
subject
Idea: analyze lingual demands with fuzzy sets

7. Demand analysis Develop sets according to their analytical structure:
Psychological types
Computer awareness
Course species
Student’s location
Analyze external restrictions and assumptions
Demand analysis

8. Example: a Set based on student’s psychological portrait
Introvert
Shy
Extrovert
Self-organized
Sloven
Example: a Set based on student’s psychological portrait

9. s(1) named “Student’s demands to training process” contains, as a partial example: {trainer control, trainer support, use practical cases, control labs, personal tempo, group discussions…}
s(2) named “Psychological types of students”: {introvert, extrovert, shy,…}
s(3) named “Student’s resource (time and money) constraints”: {decrease travel expenses, less time, convenient schedule, flexible schedule, low training costs,…}
s(4) named “Technical demands”: {remote access, desktop broadcasting, collaboration tools, recording, broadcasting system, video services, …}
… more than 20 primary sets established totally
Examples of other sets

10. Creating fuzzy elements (primary formalization)
A is a primarily formalized fuzzy set, a(i)€A
Examples:
a(dte-ra) =>s(1)=“decrease travel expenses” U s(4)= “remote access”.
a(s-cm)=>s(2)=“shy” U s(4)=“collaboration tools” U s(1)=“control me”.
According to the expert estimate and further practice we defined:
a(dte-ra)(s1)=90%, a(dte-ra)(s4)=50
a(s-cm)(s1)=20%, a(s-cm)(s2)=80%, a(s-cm)(s(4)=60%.
Creating fuzzy elements (primary formalization)

11. Optimization model H(A)  max; H(A)=a[i]|a[i]; a[i], H(a[i])0;
a[i], H(a[i])  max; (2)
ia[i],ia[i];
f(i)= ii,
where ,  and  are fuzzy operators according to [1]
Optimization model

12. Optimization criteria
Client satisfaction
Costs
Trainer’s satisfaction
Technical opportunities and constraints
Client constraints
Optimization criteria

13. A
Optimization distribution: x- real area (customer and organizational attributes) y – imaginary area (technical attributes and restrictions)

14. Converting optimal point attributes to a set of training features
maximum point A has the following attributes (partial set for example):
X(A)=({dte-ra), (s-cm), (cs-rts), (eb-dv), (s-ct), (maa-ct),…}.
H(A)= √(h(dte-ra))2+(h(s-cm))2+(h(cs-rts))2+(h(s-ct))2+(h(maa-ct))2+j((h(dte- ra))2+(h(cs-rts))2+(h(cs-rts))2++(h(maa-ct))2+(h(eb-dv))2)
What do these elements mean in a nature language? It’s simple:
Decrease travel expenses using remote access tools;
Trainer control of shy students using collaboration tools;
Convenient schedule for both students and trainers and stable schedule for trainers and training center;
Translation of an electronic board and desktop video;
Collaboration tools for shy students;
Minimal additional trainer activities due to convenient collaboration tools,
Converting optimal point attributes to a set of training features

15. Result: «inClass» webinar
1 картинка – занятия в классе
2 картинка – занимайся, откуда хочешь
3 можно и на отдыхе))
Result: «inClass» webinar

16. Main features of “inclass” webinar
Students in a class and ay webinar study together and simultaneously
Both categories collaborate under trainer supervising
Training materials and lab environment are the same
Main features of “inclass” webinar

17. Webinar inclass advantages
For students:
Study in a comfortable environment
Worth for people with disabilities
Decrease travel expenses
Communicate with colleagues worldwide
For an educational organization:
More students in class
Wider group activities
Recordings fpr second use and for control
Trainer self control
Webinar inclass advantages

18. Other training methods

19. Combined class & self study
Картинки вылетают последовательно.
– левая (занятие в классе)
Доступ к электронным материалам
Общение с группой в кабинете группы
Опять вылетает общение в классе (снова занятие с преподавателем)
Combined class & self study

20. The method main features
Provide a kick-off lesson
Use webinar recordings and e-materials for self studies
One study per week/fortnight in class
Common finish
The method main features

21. Class and self study advantages
For students:
Worth for busy people
Worth for well-organized students
The method is more friendly to students
It’s cheaper
For educational organization:
Reuse of webinar recordings and training materials
Extend training schedule (for example, once a week on Saturdays or after work day time)
Worth for long-term educational programs (MBA, second high education)
Class and self study advantages

22. Open training http://www.specialist.ru Легенда:
1 картинка – занимаемся в классе, каждый самостоятельно
2 картинка – а можно и по вебинару
3 картинка – доступны всевозможные электронные материалы
4 картинка – интересующим вопросам уделяю больше внимания
5 картинка – тренер всегда ответит на вопросы индивидуально
Open training

23. Example: open group A single class group contains many virtual groups
Number of students, total
Number of webinar students
Network Infrastructure 6 2
Database administer 3 1
Network security
Database indexing 4 …
Total 16
Example: open group

24. Open training advantages
For student
Personal tempo
Worth for introverts and shy people
Individual communication with a teacher
Appropriate training schedule
For educational organization
Reuse of webinar recordings and e-learning materials
Full classes
Individual contact with students
Open training advantages

25. Teleconferences / Virtual class

26. Teleconference study advantages
For students:
No travels
Comfortable environment
Teacher’s control
Visual contact and collaboration with all group members
For educational organization
Low expenses
High level of student’s involvement and control
Teleconference study advantages

27. training method choice
Training methods /
Demands
Class based
Webinar
Open training
Combined class and self
Fast training
*** -
Cheap training * **
Personal tempo
Slovens
Introverts
Extroverts
Live far
training method choice

28. Thank you! . www.ddintsis.com, www.specialist.ru, www.i-mokymas.com