mobile learning week 2014 - unesco.org · of the world summit on the information society (wsis)...

Open mobile ambient learning(OMAL): The next generation of mobile learning for 'mobile-rich' but 'computer-poor' contexts.

Mr. Simon .N. Mwendia,

Mobile Learning Week 2014

Copyright 2014 Mwendia,Buchem (2014)

Mr. Simon .N. Mwendia,

KCA University.

Prof Dr. Ilona Buchem,

Beuth University of Applied Sciences Berlin.

Date: 19th Feb 2013.

Introduction

Digital Divide is the gap between those who have access to digital

technologies and those who do not (Hargittai,2001).

According to ITU(2012), digital divide remain significantbecause by 2011, ICT development index (IDI) value ofdeveloped countries (6.52) was double that of developingcountries (3.24).


countries (3.24).

The need to bridge the digital divide for universal broadbandinternet access is one of the key international development goals.These include millennium development goals (MDGs) and targetof the World Summit on the information Society (WSIS) (ibid).

Developing countries Developed countriesGap =3.24

Digital Divide Levels

Digital divide has 5 levels and the first 4 determine 5th(skills of the learner(Hargittai,2001):

Autonomy of use: access freedom(when,where&how).

Technical means:Differences of Technology used.

Skills:


This study focus on technical,autonomy and social divides

Social support : Availability of others for access help.

Experience:Duration of using

the media.

The ability to efficiently and effectively technology.

Technical Means DivideGlobally

By 2012, Africa SIM penetration (SIM/ pop) was 73%.

With 214%,Europe is predicted to be the global leader by 2017


(GSMA/A.T.Kearney, 2013)

Social Support Dividein Africa

- In Africa(13.4%),South Africa(25.5%) and Kenya(24.4%) are the leaders for access to mobile based social media.

- For majority,social media is more popular than Emails.


(RIA Policy Brief No 2, 2012)

Technical Means Divide in Africa

In developing countries,fixed line penetration are very low compared to mobile penetration.

For instance, fixed line penetration is < 20% for majority African

countries and vice versa in mobile penetration(>20%).


(GSMA,2011)

Although there is high mobile penetration among university students,

in developing countries there is no computer prevalence.

For instance,in E.A universities, over 90% students own mobile

phones while the ratio of PC to students is less than 10:100.

In order to access computers, some students are forced to move to

few fixed locations with internet connectivity e.g Cyber cafes(50%),

Technical and Autonomy Divides in E.A.Universities


few fixed locations with internet connectivity e.g Cyber cafes(50%), home(25%) and workplace(8%) (Kashorda&Waema 2009).

Fig1: KCA university students in Library

Autonomy Dividein Germany Universities

Although,developed countries are perceived to have adequate

ICT infrastructure, existing E-learning systems are not fully

accessible (Bernhard Kolmel & kicin, 2004). For instance, about 50% of students with disabilities in Germany

require help services e.g. vision and audio format conversion aids so as to compensate disabilities related disadvantages.


dyslexia

Physical defect

Hearing defect

(DeutschesStudentenwerk, 2013).

Current M-learning Approaches

Current forms of mobile learning aims at the following (Pacheler et al,2010; Sharples, 2006):

1.Context-sensitive learning:Interacting with learners by consideringlearner’s current context (e.g. location, activity, social relations).

2. Mixed reality learning:Enhancing the meaning of learning contentby allowing learners to participate in a media-rich environment.


by allowing learners to participate in a media-rich environment.

3. Ambient learning:Offer easy E-learning service (i.e access to highquality and context sensitive learning content at any time, anywhere and anyhow.

Ambient learning therefore combines features context sensitivelearning and mixed reality learning.

Problem Statement

According to (B. Kolmel & kicin, 2004), ambient learning is viewed

as the next generation of mobile learning(M-learning) which can be

used to enable informal and non-formal learning processes.

E-learning M-learning Ambient learning


However, existing ambient learning projects assumeavailability of adequate infrastructures,including locationdependent devices.

(e.g computers), which are not prevalent in some contexts likethe case of African based universities.

Ambient learning is therefore not yet to be adopted in suchcontexts.

Research Objectives

1. To identify the existing digital divides in learning contexts.

2. To identify appropriate mobile learning approach (s) for bridging digital gaps among university students.

L.context1 L. context2 Approach(s)

L.context1 L.context2 divides


3. To explore appropriate technologies for enabling the identified learning approach(s).

L.context1 L. context2 Approach(s)

Approach(s)

Technologies

Motivations

The need to bridge digital divide among university students

for equitable access to learning resources.

High prevalence of mobile phone usage: Mobile devices and

Digital poor Digital rich Bridge


High prevalence of mobile phone usage: Mobile devices and applications are used everyday to interact order to interact, plan, work, play and orientate (Buchem, 2012).

The need to enhance adoption of ambient learning by integrating open educational resources (OER) into personal learning environments (PLE) in 'mobile rich' but 'computer poor' contexts like the case of HE in Africa.

OB1:Technical Divides in Nairobi and Berlin


1.Gap for desktops is larger in Nairobi universities (65%) compared to Berlin Universities (36%).2.In both cases, the gaps for smart phones(21,14) are smallerthan gaps for desktops (65,36) and laptops(30,29).

Autonomy of use Nairobi and Berlin varsities

Nairobi Universities Berlin Universities


1.Text format is more popular in all cases for both male & female.2.Audio modes has low preference in both case specially older students(26-30yrs).

OB2:Proposed ML Approach

Open mobile ambient learning(OMAL) is a combination of mobile

ambient intelligence characteristics and requirements of open

learning, personalized learning and mobile learning to allow

easy E-learning service.

M-Ambient intelligence

OMAL

Easy E-learning

Rationale


Open Learning

Personalized learning

M-Learning

M-Ambient intelligence

Access Independence

Easy E-learning

Access flexibility

High quality content


Open learning: Approach that analyses needs learners and seeks to

provide learning with minimum learning barriers in terms of

accessing resources (e.g OER) (UNICEF ROSA), 2009).

Open education resources(OER):Materials free available for public access,usually under open licenses(UNESCO/COL,2011).E-learning: Deliberate utilization of ICT for teaching and


Learning (Naidu, 2006).Mobile learning: Learning by means of wireless technological devices that can be pocketed and utilised by learner on move without breaking transmission signals (Attewell & Savill-Smith,2005). Personalized learning: Learning by means of PLE(i.e. individual collocations of distributed applications, services and resources) (Buchem et al., 2011).


Ambient: relating to the immediate surroundings of something.

Mobile Ambient intelligence characteristics (Aarts,2003;Bick.et.al, 2007):

i) Embedded: resources are embedded either partially or fully on mobile media which is surrounding or in the hands of the learner.

ii) Context-awareness: Recognize user presence & their context.


iii) Personalized: Allow choice of when,how & where to access.

iv) Adaptation: Resources can change depending on learner needs

v) Anticipate: System can predict learner desires.

vi)Interconnection: Wireless interconnection of mobile devices and Systems.

(Koninklijke Philips N.V., 2014)

OB3:Proposed Technologies

Mobile ambient intelligence technologies (MAIT): Refers to

technologies that use mobile media(e.g mobile phones) to provide

ambient intelligence characteristics .

They can therefore be used to enable OMAL.


OB3:Proposed Technologies

Example: Phone centric ambient intelligence technologies (MCAIT)

use phones with context sensitive apps to provide Intelligent

services(Maheshwaree,2008). E.g Adaptable Mobile PLE .OER Cloud

learner

OERcloud

Learner


OER Cloud: collections of OER e.g OER Knowledge cloud

AMPLE

Context

learner

Author

Learner

Target Groups

1.Social Poor,who have limited networks to people that can help to learn.

2.Economic poor,who can only afford to access low-end phone.

3.Computer Poor,who have poor access to PC but rich access to phones.


(Beuth,2012;Visual photos.com; ETU ,2011;Gatehouse.G.,2012)

Germany and Kenya users with different media.

Target Groups

4. Students with special needs (e.g disabled, elderly) to

enhance their learning independence.

dyslexia

Physical defect

Hearing defect


defect

(DeutschesStudentenwerk,2013).

Scenario Representation


Study References

Research blog

Research details can be accessed using the following link:

http: cbalgroup.wordpress.com/home-2/

Publications

1. Mwendia, S., Waiganjo, P., Oboko, R., 2013. 3-Category


1. Mwendia, S., Waiganjo, P., Oboko, R., 2013. 3-Category Pedagogical Framework for Context Based Ambient Learning, in: IST-Africa 2013 Conference Proceedings. Presented at the IST Africa, IEEE.

2. Mwendia, S., Wagacha, P.W., Oboko, R., 2014. Culture Aware M-Learning Classification Framework for African Countries, in: Cross-Cultural Online Learning in Higher Education and Corporate Training. IGI Global, Pennsylvania,USA, p. 14.

Contacts: Mr Simon Nyaga Mwendia

Kca University

[email protected].

Prof Dr ilona BuchemBeuth University of Applied sciences Berlin

End


Beuth University of Applied sciences Berlin [email protected]

Thank You.

Questions ?

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