Overview

1. Intelligent electronic platforms for learning and training: learning styles, adaptation and personalization • M. Rangoussi(1), N. Stathopoulos(1), H. Simos(1), K. Zachariadou(2), D. Metafas(1), A. Charitopoulos(1), P. Kervalishvili(3) • (1) Dept. of Electronics Engineering, TEI Piraeus, Greece • (2) Dept. of Physics, Chemistry and Materials Technology, TEI Piraeus, Greece • (3) Dept. of Physics, Georgian Technical University, Georgia • GTU, Tbilisi, Georgia, 6-9 June 2013, • ‘SENS-ERA’ FP7 Project • “Strengthening sensor research links between the Georgian Technical University and the European Research Area”

2. Overview • Introduction • Intelligent environments and the ‘student model’ • E-training on Sensors: development of an electronic environment • Inducing intelligence by student classification: a work in progress • Discussion and Conclusion

3. Introduction • E-learning and e-training platforms are attractive alternatives to conventional instruction or training methods- feasible and affordable thanks to recent science and technology advances- service level application, on the cross-section of several disciplines • Practical advantages over conventional tools and methods • reusability of the training material across • different audiences • different courses • different time periods • Cross-section of ICT with cognitive and learning psychology principles- behaviourism, constuctivism, social learning, collaborative learning - student-centric model instead of teacher-centric model- human instructor assumes the role of mediator / facilitator- human instructor adaptivityto the students needs: automatic by intuition and experience- electronic instructor adaptivity: through induced ‘intelligence’ by its designers- ‘senses’ the feeling, attitudes and responses of students- ‘reacts’ by adaptation in real time, according to a predefined set of rules

4. Intelligent environments and the ‘student model’ • Modern, high tech e-learning and e-training platforms are designed to be ‘smart’ or ‘intelligent’ – artificially, of course. • Methods and approaches employed for AI:- expert systems- artificial neural networks- fuzzy logic • - intelligent agents technologies- any other ‘soft computing’ approach • Examples of existing intelligent e-learning or e-training systems- Intelligent Tutoring Systems (ITS)- Content Management Systems (CMS), Learning Management Systems (LMS) • Core software module: the ‘student model’ extractor 1. Level and previous knowledge of the student on the taught material (e.g., advanced, skilled, of elementary knowledge, etc.) Specific gaps in ‘chapters’ of the material; • 2. Personality type of the student; • 3. Cognitive style of the student; and • 4. Learning style of the student. • The ‘matching hypothesis’ scientific dispute - no studies available with statistically significant results to justify the extra costs- learning style awareness instead of accomodation

5. E-training on sensors: development of an electronic environment • SENS-ERA FP7 project framework, e-training modules development • Material contributed and structured by TEI Piraeus SENS-ERA members • E-training modules structure- Radiation Sensors Course • • Radiation Interactions • • Radiation Sensors • • Gas-filled detectors • • Scintillation detectors • • Semiconductor detectors • • Calorimeters • • Radiation Sensor Electronics • - Optical Sensors Course • • Optical Sensors (general part) • • Fiber Bragg Grating Sensors

6. E-training environment: Introductory pages of the moodle platform

7. E-training courses: Introductory pages, moodle platform

8. E-training modules: sample introductory pages to modules

9. Advantages offered to the designer and developer, the instructor and the trainee The advantages of offering the training material in an electronic form and under an e-learning platform are multiple: • The material can be reused at different times or different audiences to cover different training needs. • The material can be easily and readily restructured and/or enriched and/or improved according to needs. • The material can be accessed remotely and asynchronously, thus offering degrees of flexibility to the training process. • The material can be distributed to a practically infinite number of trainees at no extra costs other than that of developing and of maintaining the server and the service.

10. Advantages offered to the instructor and the trainee • • Students or trainees subscribe to the training module(s) according to needs, interests or counseling provided. Access is offered through individual access codes and passwords. The personal progress in the training material can therefore be easily monitored by the software and registered for inspection by the instructor. • • Self assessment and class/group evaluation tests can also be delivered through the same environment and scored automatically or manually according to their nature and to the instructor’s choices. • • Student or trainee counseling and help can be offered through the same environment by the instructor either at an individual or group basis. • • The communication tools incorporated in the platform allow the development of forums and chats shared among the group of students or trainees and their instructor. Through these tools discussion, problem solving, help and advice are exchanged among all members of the platform. These tools support collaboration and enhance the learning or training experience. • • Statistics held by the platform as to the results and use of the material across different groups and different periods help the instructor to redesign the material and methods accordingly. • • Gallops on the opinions of students or trainees on the environment and its functionalities as well as on the material and its adequacy can be delivered through the same platform and offer feedback for its improvement.

11. Inducing intelligence by student classification: work in progress • Pre-tests in order to classify students or trainees objectively (depth and breadth of prior knowledge in the field of training) • Post-tests for evaluation, feedback to redesign application- they do not contribute to the intelligent character of the environment

12. Inducing intelligence by student classification: work in progress • An automatically graded classification quiz from the Radiation Sensors Course

13. Discussion and Conclusion • Pre-test functionality may serve different purposes- Major purpose: to objectively classify trainees as to the depth and breadth of their prior knowledge of the field.- refinement and standardization of the classification process is necessary,- maximization of the ‘ratio’ of learning outcomes from training over time spent on training. • Training module-specific pre-test have to be designed and distributed • Statistically significant results through the use of the platform by real users are necessary to evaluate its functionality and dynamics • A hierarchy of classification decisions in depth (e.g., three levels) and in breadth (so many areas as the individual training modules are) for objective classification • Evaluation of the progress towards these targets can also be developed through the same platform.