Learning Traces, Learning Clocks and Artificial Observers

1. Learning Traces, Learning Clocks and Artificial Observers Alvarez-González, Luis A. Universidad Austral de Chile. November, 2007

2. Content • Introduction • Learning System Model • Learning Traces • Learning Clocks • Artificial Observer • Conclusions

3. Introduction • CSCL, emerges as a discipline 1989. • The concepts of Learning Objects was developed at the beginning of 2000. • The standard IMS – LD of Learning Design was published at the beginning of 2003. • The first learning design management system appears in 2002. And good example is LAMS.

4. Introduction • The learning design managements systems are a good set of tools. However, are not enough to know the behavior and aid to students group, specially in large groups. • It’s important to know the learning trace (Unit of Learning completed by students) of each one and the learning trace of the group as a whole, in an asynchronous environment. • Where the learning trace is a details……

5. Introduction • Finally, A good learning depends mainly on a good design and the teacher. ¡The learning technologies can only aid to teachers ! • So, in this proposal a good learning design is assumed.

6. Introduction How to do that ? The task could be complex and we need a theoretical model to help us.

7. Learning System Model

8. Student 1 Observer Student 2 LDMS on a server Student 3 Tutor Student 4 Learning System Model Learning System is a team of collaborators using a learning design management system. Collaborators are: • Students. Following a learning design. • Artificial observer. Looking the learning process. • Tutors. Guiding the learning process. All of they interconnected by a network The Learning Designs Managements Systems will be on a server.

9. a1 a2 a3 a2 a1 a3 Learning System Model An learning design could be considered as sequence of learning activities ai where every activity can be one learning object or several optional learning objects. And each student following the sequence

10. Learning System Model a1 a2 a3 Note that the four types of LO are in the figure: 1. Instruction 2. Colaboration 3. Simulation and 4. Evaluation a4 Luis Alvarez Felipe Zapatero Pedro Pérez a5 a6 Juan González a7 a8 a9

11. LearningTraces

12. Learning Traces • In an asynchronous system there exist no constrains on the learning relative speed. However, is important to help the “slow students”. • If we know the sequence of learning activities completed by an student (learning trace) is possible to know the group learning trace.

13. Learning Traces a1 a2 a3 a4 a5 a6 a7 a8

14. Learning Traces Let, learning trace for the student i is the ordered set of activities completed by student i: ti={ai1 ai2…. } • And learning trace of all students as a set T = t1 t2 …tn.

15. Learning Traces a1 a2 a3 a4 a5 a6 a7 a8 t(Pedro Perez) = { a11, a21, a31 , a41, a51}; t(Luis Alvarez) = { a12, a22, a32 }; … T = t(Pedro Perez)  t(Luis Alvarez) t(Felipe Zapatero)  t(Juan Gonzalez);

16. Learning Clocks

17. Learning Clocks • Today, usually the students do many things at the same time. • Every students has his/her own “learning speed” and then own “learning clock”. • The “learning speed” is not constant. Could be fast at the beginning a slow at the end. Moreover, depend of external factors.

18. Learning Clocks • A single natural number is sufficient to represent the set ti(a) • Learning traces can be represented as 2- dimensional vector ti= (uidi , lcki ) • uidi: unique user identifier, of the student i • lcki: learner clock of the student i. • The entire learning trace can be represented by an 2 x n dimensional matrix. • T=[ t1T, t2T,….., tnT ]

19. Learning Clocks • Each “learning clock” will be incremented with every learning activity carried out for corresponding student. • So, for example the learning clocks are: • For Pedro Perez, t1 = (50,5) • For Luis Alvarez, t2 = (53,3) • For Felipe Zapatero, t3 = (51,5) and • For Juan Gonzalez t4 = (49,6) NOTE: ¡ Luis Alvarez need help!

20. Learning Clocks (50,4) (50,2) (50,3) (50,5) (50,1) (53,2) (53,3) (53,1) (51,4) (51,5) (51,2) (51,3) (51,1) (49,2) (49,3) (49,4) (49,5) (49,6) (49,1)

21. Learning Clocks • And the Group Trace will be T = [ t1T, t2T, t3T, t4T]=

22. Artificial Observer

23. Artificial Observer • Looking for the process. • “Measuring of knowledge” of each one and the group: • Quantity of learning activities carried out for each one and the all group. • Notifying to Tutor the progress of the group and the each student in the learning design.

24. Artificial Observer Artificial Observer Student 1 T = [ t1T, t2T, t3T, t4T] t1 = (50,5) T = Student 2 t2 = (53,3) Learningware on a server Student 3 t3 = (51,5) Tutor Student 4 t4 = (49,6)

25. Artificial Observer • The Artificial Observer periodically will do a multicast to the lesson; where a lesson is a learning design running in a LDMS; and each learning process will reply with the learning clock and eventually others parameter as score for each learning event. • And it send to tutor; by LMS; the: • Learning trace for each one and for the all group. • The LMS can calculate parameter as speed learning average, progress average, build learning curves, etc. With this information the tutor can re-design the learning activity. • Will be one observer for each lesson.

26. Conclusions

27. Conclusions • A model of asynchronous communication for learning groups have been developed. • Knowledge from other areas can be used in CSCL, in this paper we use concepts and definitions adapted from process communication in distributed systems.

28. Future Work. • We must develop: • Artificial observers. • And so on…..

29. Thank you for your contributions