practical considerations for dedicated e-learning...
TRANSCRIPT
Practical Considerations for
dedicated E-Learning
Applications
Mircea-Florin Vaida, TUC-N
ETTI- COM Department
6-10 July 2015, Univ. de Savoie, SYMME
MontBlanc lab., Chambery, France
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Agenda
E-learning platform using NLP profile
GAEM web application
Footprint algorithm
Final conclusions
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First e-Learning platform based on
NLP profile
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Implementation
Scenario
• The course is presented on slides.
• Some areas of each slide capture the attention of certain students
• This is measured by analyzing their eye movements, captured by integrated webcams
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Implementation
Scenario
• Visual information is acquired by using an eye tracking system
• If more students encounter the same issue, the teacher is notified so that he can insist on explaining it to the students
• At the end of the class, a report is generated based on the recorded difficult sections for later review
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Implementation
Block Diagram of Track2D
User
Eye Tracking
CalibrationData
miningDifficulty detection
Results
PresentationEye
TrackingGazer
Difficulty detection
Results
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Implementation
Calibration Block Diagram
UserImage
Acquisition Calibration
DecisionResults
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Implementation
Gazer Block Diagram
User Presentation Gaze Tracking
Difficulty detectorResults
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Experimental Results
Calibration
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Experimental Results
Track2D Gazer
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Slide design requirements:
•Lightweight slides•Meaningful content•Few words•Representative images
Experimental Results
Heatmap
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Heatmap generated based on Track2D data Heatmap generated with a free tool
Conclusions
Limitations
Eye tracking in different positions of the head relative to thewebcam can be difficult or impossible.
Eye tracking is a process dependent on the lightingconditions. Extreme lighting conditions should be avoided
Because the software used for eye detection –MachinePerception Toolbox (MPT) – is open-source and still a work-in-progress it may malfunction, have errors or performanceproblems
No distribution of the didactical material yet implemented Restrictions imposed to the students while using thissoftware
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Conclusions
Upsides
Low-cost application Can run on any Operating System that MPTsupports
Good toleration to lighting conditions Does not use intrusive techniques or make theuser wear headgear
Gives objective feedback on the learning process Can be easily modified for other applications Track2D’s modularity provides an easy way tochange components when others more productiveare available
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Further Improvements
Increase precision and decrease erroneous datainput
Ability to control the mouse cursor using the gazemarker
Control of mouse click by blinking, reducing inputtime
Integrate Track2D in an E-learning system
Centralized system to open the same slide on allthe laptops in the course room
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Web Grouping based on Enneagram
and Myers –Briggs Type Indicator
GAEM
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Architecture
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Used Technologies
PHP (CodeIgniter Framework) - MVC
HTML 5
JavaScript
Ajax
MySQL
jQuery & jQuery UI
Twitter Bootstraps
CSS
HighCharts
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Grouping Algorithms
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Application Structure
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Conclusions We propose a grouping strategy for e-learning
environments and research teams based on psychological features. Effective grouping strategies take into account personality factors, which are hard to be evaluated by a computer.
An original aspect of our work is the use of typologies, determined according to the Enneagram methodology test. A refinement is considered based on a MBTI test.
The Enneagram and MBTI tests, with the classification process are determined in an automated mode using a web dedicated application, GAEM.
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Footprint
New algorithm used in grouping process based on a web e-
learning platforms
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The web application is composed by the following components:
Student Interface Instructor Interface Ontology (Data base) Grouping and evaluation engine
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Excepţii
Autentificare şi autorizare
Logout
Modif. User
Grupare
Modif. Grupe
Logout
Login
Înregistrare
Portal
Student
Portal
Profesor
Test
Test
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Circular VectorsEnneagram: Integration direction + MBTIWill significantly reduce the number of checkingThe next element will be verified only with the last element from the team
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Classification Results The grouping algorithm called “Footprints”, that it’s a semi-
opportunistic grouping mechanism will be integrated to create the working groups. Our first tests show that the efficiency of the algorithm is about 93%.
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The solution adopted is complete in terms of creating optimal working teams and their management, but the application can be developed further
The average efficiency of the algorithm proposed is 93%, so qualify as high-efficiency algorithm
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Possible future directions would consist of : -integrating new grouping strategies based
on Yin/Yang and QI constitutions including the 5 Elements from different traditions
-extending the proposed solutions, in order to obtain a complete e-learning system
-using other advanced technologies for a consistent system
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Thank you.
Slide 31