Mobile Learning Africa (UNESCO)
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Mobile Learning Africa (UNESCO)

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Presentation given at the UNESCO Mobile Learning Week 2014 (Paris, France).

Presentation given at the UNESCO Mobile Learning Week 2014 (Paris, France).

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Mobile Learning Africa (UNESCO) Mobile Learning Africa (UNESCO) Presentation Transcript

  • 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)
  • 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)
  • 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)
  •   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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • Scenario Representation Copyright 2014 Mwendia,Buchem (2014)
  • Scenario Representation Copyright 2014 Mwendia,Buchem (2014)
  • Scenario Representation Copyright 2014 Mwendia,Buchem (2014)
  • 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)
  • 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)