Practical Considerations for

dedicated E-Learning

Applications

Mircea-Florin Vaida, TUC-N

ETTI- COM Department

Mircea.Vaida@com.utcluj.ro

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