1. The document summarizes a presentation on open mobile ambient learning (OMAL) as a way to bridge digital divides, especially the technical and autonomy divides experienced in universities in Africa and Germany. 2. OMAL is proposed as a combination of mobile ambient intelligence, mobile learning, personalized learning, and open learning to provide easy access to high-quality educational resources through mobile phones. 3. Phone-centric ambient intelligence technologies that use context-aware apps are suggested to enable OMAL by connecting learners to open educational resource clouds through adaptable mobile personal learning environments.

1. Mobile Learning Week 2014
Open mobile ambient learning(OMAL): The next generation of
mobile learning for 'mobile-rich' but 'computer-poor' contexts.
Mr. Simon .N. Mwendia,
KCA University.
Prof Dr. Ilona Buchem,
Beuth University of Applied Sciences Berlin.
Date: 19th Feb 2013.
Copyright 2014 Mwendia,Buchem (2014)

2. 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 significant
because by 2011, ICT development index (IDI) value of
developed countries (6.52) was double that of developing
countries (3.24).
Developing countries
Gap =3.24
Developed countries
The need to bridge the digital divide for universal broadband
internet access is one of the key international development goals.
These include millennium development goals (MDGs) and target
of the World Summit on the information Society (WSIS) (ibid).
Copyright 2014 Mwendia,Buchem (2014)

3. Digital Divide Levels
Digital divide has 5 levels and the first 4 determine 5th(skills of the
learner(Hargittai,2001):
Autonomy of use:
Technical means:
access freedom
Differences of
(when,where&how)
Technology used.
.
Skills:
The ability to efficiently
and effectively technology.
Experience:
Duration of using
the media.
Social support :
Availability of others
for access help.
This study focus on technical,autonomy and social divides
Copyright 2014 Mwendia,Buchem (2014)

4. 

Technical Means Divide
Globally
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)
Copyright 2014 Mwendia,Buchem (2014)

5. Social Support Divide
in 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)
Copyright 2014 Mwendia,Buchem (2014)

6. 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)
Copyright 2014 Mwendia,Buchem (2014)

7. Technical and Autonomy
Divides in E.A.Universities
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%),
home(25%) and workplace(8%) (Kashorda&Waema 2009).
Fig1: KCA university students in Library
Copyright 2014 Mwendia,Buchem (2014)

8. Autonomy Divide
in 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
Hearing defect
Physical
defect
(DeutschesStudentenwerk, 2013).
Copyright 2014 Mwendia,Buchem (2014)

9. 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 considering
learner’s current context (e.g. location, activity, social relations).
2. Mixed reality learning:Enhancing the meaning of learning content
by allowing learners to participate in a media-rich environment.
3. Ambient learning:Offer easy E-learning service (i.e access to high
quality and context sensitive learning content at any time, any
where and anyhow.
Ambient learning therefore combines features context sensitive
learning and mixed reality learning.
Copyright 2014 Mwendia,Buchem (2014)

10. 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 assume
availability of adequate infrastructures,including location
dependent devices.
(e.g computers), which are not prevalent in some contexts like
the case of African based universities.
Ambient learning is therefore not yet to be adopted in such
contexts.
Copyright 2014 Mwendia,Buchem (2014)

11. Research Objectives
1. To identify the existing digital divides in learning contexts.
L.context1
divides
L.context2
2. To identify appropriate mobile learning approach (s) for bridging
digital gaps among university students.
L.context1
Approach(s)
L. context2
3. To explore appropriate technologies for enabling the identified
learning approach(s).
Approach(s)
Technologies
Copyright 2014 Mwendia,Buchem (2014)

12. Motivations
The need to bridge digital divide among university students
for equitable access to learning resources.

Digital poor


Bridge
Digital rich
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.
Copyright 2014 Mwendia,Buchem (2014)

13. 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 smaller
than gaps for desktops (65,36) and laptops(30,29).
Copyright 2014 Mwendia,Buchem (2014)

14. 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).
Copyright 2014 Mwendia,Buchem (2014)

15. 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.
OMAL
Rationale
M-Ambient intelligence
M-Learning
Personalized learning
Open Learning
Easy E-learning
Access Independence
Access flexibility
High quality content
Copyright 2014 Mwendia,Buchem (2014)

16. OB2:
Proposed ML Approach
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).
Copyright 2014 Mwendia,Buchem (2014)

17. OB2:
Proposed ML Approach
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)
Copyright 2014 Mwendia,Buchem (2014)

18. 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.
Copyright 2014 Mwendia,Buchem (2014)

19. 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
OER
Cloud
cloud
learner
Learner
AM
PLE
Context
Author
OER Cloud: collections of OER e.g OER Knowledge
cloud
Copyright 2014 Mwendia,Buchem (2014)

20. 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.
Germany and Kenya users with different media.
(Beuth,2012;Visual photos.com; ETU ,2011;Gatehouse.G.,2012)
Copyright 2014 Mwendia,Buchem (2014)

21. Target Groups
4. Students with special needs (e.g disabled, elderly) to
enhance their learning independence.
dyslexia
Hearing defect
Physical
defect
(DeutschesStudentenwerk,2013).
Copyright 2014 Mwendia,Buchem (2014)

22. Scenario Representation
Copyright 2014 Mwendia,Buchem (2014)

23. Scenario Representation
Copyright 2014 Mwendia,Buchem (2014)

24. Scenario Representation
Copyright 2014 Mwendia,Buchem (2014)

25. 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
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.
Copyright 2014 Mwendia,Buchem (2014)

26. End
Contacts: Mr Simon Nyaga Mwendia
Kca University
smwendia@yahoo.com.
Prof Dr ilona Buchem
Beuth University of Applied sciences Berlin
buchem@beuth-hochschule.de
Thank You.
Questions ?
Copyright 2014 Mwendia,Buchem (2014)