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Andreea Molnar, Cristina Hava Muntean

School of ComputingNational College of Irelandamolnar@student.ncirl.ie,

cmuntean@ncirl.ie

QoE-MOT – A Learner Quality of Experience-

Oriented Authoring Tool

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Overview

Adaptive e-Learning Systems (AeLS)Multimedia Content IssuesAuthoring AeLSQuality of Experience LOAS (QoE-LAOS)

FrameworkQuality of Experience MOT (QoE-MOT)

Authoring ToolConclusions & Future Work

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Adaptive e-Learning Systems

Adaptive e-Learning Systems

The main goal is to provide personalised educational content tailored to learner’s profiles

Adaptation is based on:Learner profileLearner deviceLeaner contextLearner network

performance, etc

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Multimedia Content Issues (1)

Increasing useof multimedia in e-learning courses

Not all people perceive multimedia quality in the same way

Educational material, especially video, is affected by poor QoS (Quality of Service):Unclear voiceUnclear imageSynchronisation problems between voice and

videoDelay in accessing the educational content

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Multimedia Content Issues (2)

Is there a solution?Take into account the characteristics of

the multimedia educational content as well as

network and learner device in the course design and adaptation rules specification

phases.

An authoring tool which would help the course creators to create this kind of

course.

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Authoring AeLS

Type of authoring toolsdo not support content adaptation:

HyCo –a simple and intuitive interface for generating learning content

MEAT – it allows single authoring for modal interfaces

support content adaptation:MOT (My Online Teacher) – a general

authoring systemA-SCORM Course Creator Tool – creates

adaptive SCORM conformant courses

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QoE-LAOS FrameworkAn extension of LOAS authoring model that

has 5 layers:Domain Model (DM)User Model (UM)Goal and Constraints Model (GM)Adaptation Model (AM)Presentation Model (PM)

Adds three new sub-layers QoE Content Features sublayerQoE Characteristics sublayerQoE Rules sublayer

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QoE-MOT (Quality of Experience-MOT)

QoE-MOT is based on the generic authoring tool: MOT

Support for creation of performance-aware adaptive courses

It follows the QoE-LAOS authoring model

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QoE-MOT: Content Features (1)

Stores various versions with different properties for each file

File characteristics metadata are also keptText: size, length, format (e.g. plain text)Image: size, format (e.g. jpg), resolutionWebpage: size, no. of objects embedded in a

fileVideo: bit rate, frame rate, resolution,

encoding scheme

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QoE Content Features (2)QoE-MOT Exemplification

New metadata for the

multimedia version 1 file

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QoE-MOT: QoE Rules (1)

Defines adaptation rules based on the QoE characteristics

Different strategies can be introduced at this level, providing the author with the possibility to choose between them

The rules are written using LAG adaptation language

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QoE-MOT: QoE Rules (2)Exemplification:

if enough (DM.Concept.access DM.Concept.type == bitrate ,2)then (if DM.Concept.bitrate <=maxbitrate t hen UM.GM.suitable = true else UM.GM.suitable = false)

if enough (DM.Concept.access UM.GM.suitable == true ,2)then PM.GM.Concept.show = true

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Conclusions

Multimedia content can be damaged by the network condition

QoE-MOT an authoring tool for the AeLS was presented

QoE-MOT is based on the QoE-LAOS authoring model

It extends MOT with new features that allow QoE aware rules adaptation authoring

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Future work

To automate the process of content metadata extraction

Reusable adaptation strategies for authoring the content have been proposed and will be further tested

Subjective tests will be performed to evaluate QoE-MOT

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Questions?