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Item Ambient learning conceptual framework for bridging digital divide in higher education(IGI Global, 2014) Mwendia, Simon N.; Wagacha, Peter W.; Oboko, RobertAccording to ITU (2012), digital divide is the difference between countries in terms of levels of ICT development. This difference remains significant. In 2011, the ICT Development Index (IDI) value of developed countries (6.52) was twice as high as that of developing countries (3.24). The need to link the digital divide for universal broadband Internet access is within the key international development goals, which include World Summit on the Information Society (WSIS) goals and Millennium Development Goals (MDGs). Ambient learning is the next generation of M-learning (Bick, Kummer, Pawlowski, & Veith, 2007), which allows flexible content access by considering learner's current situation and learning context (Kofod-Petersen, et al., 2008). However, ambient learning has not yet attained a state of common understanding (Winker, Scharf, Hahn, & Herczeg, 2011) and is not widely used or adopted (Bick, et al., 2007). This chapter presents a theoretical conceptual framework to foster creativity for innovative ambient learning applications, which can be used to bridge the digital gap between universities in developed and developing countries.Item Culture aware M-learning classification framework for African countries(IGI Global, 2014) Mwendia, Simon N.; Wagacha, Peter W.; Oboko, RobertAfrican countries are currently experiencing proliferation of mobile phone subscriptions but no prevalence of personal computers or electricity (Parker, 2011). It is estimated that, by the end of 2015 in Sub-Saharan Africa, the percentage of people with mobile network access will surpass that of access to electricity in homes (Rao, 2011). This phenomenon is also experienced in learning institutions, particularly universi- ties, where almost every student owns a mobile phone (Kashorda & Waema, 2009). Although there is a great potential for Mobile Learning (M-Learning) in education, the formal integration of M-Learning in the education systems is in its infancy since there is limited number of M-Learning projects in the region. This is in contrast with the rapid increase and integration of mobile phones in the daily lives of the population in the region (Isaacs, 2012). According to Olaniran (2009), online learning needs to be culturally aware and investigate the dimensions of cultural variability as well as its influence on learning within global education. In an attempt to address this need, this chapter focuses on the African region in describing dimensions of cultural variability and proposes four categories for M-Learning projects as well as their influences on dimensions of cultural variability.Item Evaluation model for improving ambient learning systems towards achieving sustainable development goal four(2018) Mwendia, Simon N.Among the 17 sustainable development goals specified by United Nations organization in 2015, goal four is the key for progress towards the achievement of all the other goals. However, studies show that this goal is yet to be achieved among African universities in terms of supervision due to inadequate availability of supervisors to their research students. Ambient learning approach promises to address the problem by allowing access to education services like research supervision at anytime, anywhere and anyhow. Nevertheless, little research has been conducted to assess its effectiveness towards achieving sustainable development goal four. The aim of this paper is to describe a model that illustrates how ambient learning systems can be combined with decision support tools to support evaluation of its effectiveness.Item Ambient learning - knowledge as a service model: towards the achievement of sustainable development goal four(IEEE, 2018) Mwendia, Simon N.; Karanja, Richard G.Studies show that United Nations Sustainable Development Goal Four is yet to be achieved. This paper presents an artefact named “Ambient learning- Knowledge as a Service model” for describing how actionable knowledge can be extracted from ambient learning systems to support improvement and consequently facilitate the achievement of Sustainable Development Goal Four. A creative process was adopted to guide the development of the model. The process involved carrying out problem analysis through literature review, designing the model by combining ambient learning and Knowledge as a Service concept and demonstrating its application by developing a prototype. Evaluation results revealed that C4.5 algorithm that is implemented in Waikato Environment for Knowledge Analysis (WEKA) software is suitable for extracting knowledge from ambient learning systems while Swi-prolog software can be applied to create a tool for knowledge delivery.Item Dynamic heuristics greedy search: a mobile information retrieval algorithm for ambient learning systems(ACM Digital Library, 2016) Mwendia, Simon N.; Oboko, Robert; Wagacha, Peter WaiganjoItem Open mobile ambient learning(OMAL): The next generation of mobile learning for 'mobile-rich' but 'computer-poor' contexts(DAAD, 2014) Mwendia, Simon N.; Buchem, IlonaBy the end of year 2011, Africa had over 620 million mobile connections, overtaking Latin America to become second largest mobile market after Asia. According to Ilona Buchem in 2012, since mobile devices and applications are used every day in order to interact, plan, work, play and orientate, mobile pedagogies in context of HE in Africa should focus more on ambient assisted learning to facilitate greater independence and improve quality of life, which is especially beneficial to learners with special needs (e.g. disabled people and people living in remote locations). African universities face challenges in their attempts to offer quality educations, including the lack of access to university educational facilities and scientific information, poor access to computers, scarce availability of qualified teachers and the irrelevance of formal education to African needs, according to research conducted in 2008 and 2009. This removes flexibility that is needed in personalized learning, according to a 2010 study. This calls for innovative learning approaches that facilitate flexible access of open education resources (OER) in settings with high prevalence of mobile devices (such as mobile phones) but poor prevalence of location dependent devices (such as computers) as it is the case in Africa. Current forms of mobile learning aim at enabling context-sensitive learning, e.g. interacting with learners by considering learner’s current context (e.g. location, activity, social relations), mixed reality learning, for example, enhancing the meaning of learning content by allowing learners to participate in a media-rich environment, as well as ambient learning, for example, delivering learning content at anytime, anywhere and anyhow by placing digital artefacts within the environment of the learner, according to a 2006 report. However, a number of European projects in this area assume availability of adequate infrastructures, such as location dependent devices, which are hard to implement in setting such as African based universities, given the lack of sophisticated technological infrastructures. This presentation focuses on mobile learning as a means for supporting advancement in the quality of education by addressing mobile pedagogies that provide flexible access to learning through consideration of learner’s current context. Based on the Mobile Interface Ambient Learning (MIAL) framework, according to a 2013 report, designed for contexts with high penetration of mobile devices (mobile rich) but low prevalence of location dependent devices (computer poor), we propose Open Mobile Ambient Learning (OMAL) as an approach to enhance adoption of ambient learning by integrating Open Educational Resources (OER) into Personal Learning Environments (PLE), e.g. individual collocations of distributed applications, services and resources, according to a 2011 study, in context of HE in Africa. OMAL targets to benefit university students with special needs (e.g. disabled, elderly) by improving their learning independence and digital marginalization (e.g. own phones but have poor access to computers) through enhancing access flexibility. OMAL combines mechanisms of embedding intelligent interface in mobile devices to monitor special learning needs and contexts (Mobile Ambient Intelligence), with mechanism of appropriating adaptable learning tools and services by learner through mobile devices Adaptable Mobile Personal Learning Environment (AMPLE) and mechanisms of dynamically discovering Personal Learning Networks (PLN) in OER driven environments.Item 3-Category pedagogical framework for context based ambient learning(IEEE, 2013) Mwendia, Simon N.; Waiganjo, Peter; Oboko, RobertMobile phones have taken centre stage in transforming people’s lives in all sectors of African economies. With regard to Education sector, studies show that, there is high prevalence of mobile phones among learners in African universities but no computer prevalence. However, E-learning technologies are not readily available among learners. Learners are therefore forced to access content from few fixed locations with internet connectivity such as cyber cafes and workplace, eliminating access flexibility in learning. The ‘Mobile phone rich’ but ‘computer poor’ context prevailing in African universities presents an opportunity to establish an appropriate type of learning that utilizes mobile phones rather than computers. This paper explores existing categories of m-learning projects and proposes a 3-category framework to provide better understanding of ambient learning and allow integration of future ambient learning projects situated in different learning environments.Item An empirical approach to mobile learning on mobile ad hoc networks(Institute of Electrical and Electronics Engineers (IEEE), 2020) Mwendia, Simon N.; Ichaba, Mutuma; Musau, FelixMobile Ad hoc Networks (MANETs) are made up of mobile nodes that are interconnected wirelessly, while topology changes as mobile nodes join and leave the network. MANETs do not depend on fixed infrastructure. Due to their dynamism and low cost (no infrastructure is needed), MANETs have been proposed as a mechanism suitable for carrying out mobile learning (m-Leaning) in developing countries. However, systematic literature review indicates that the existing MANETs-based m-Learning models are disadvantaged because they fail to identify possible routing protocols able to support such models. As a result, it becomes very difficult to implement the existing MANET-based m-Learning models. This paper characterizes MANETs-based m-Learning proposed by [1]. Thereafter, it uses area, nodes, and data packets information as basic scalar parameters on Zone Routing Protocol (ZRP) simulated on NS-2 and ZRP code supplemented with positional and directional information of nodes in the Intrazonal Routing Protocol (IARP) on OMNET++. According to simulation results, a directional-positional enhanced ZRP outperforms regular ZRP on packet delivery ratio, delay and overall data packet throughput. Results from the simulation suggests that a supplemented ZRP is a feasible routing protocol for supporting m-Learning in a typical university campus based on the identified basic scalar parameters and characterization of [1].Item Applying Data Mining in Graduates’ Employability : A Systematic Literature Review(International Journal of Engineering Pedagogy, 2023) Mburu, Lucy W.; Mwendia, Simon N.; Mpia, Héritier N.Envisaging an adequate IT/IS solution that can mitigate the employability problems is imperative because nowadays there is a high rate of unemployed graduates. Thus, the main goal of this systematic literature review (SLR) was to explore the application of data mining techniques in modeling employability and see how those techniques have been applied and which factors/variables have been retained to be the most predictors or/and prescribers of employability. Data mining techniques have shown the ability to serve as decision support tools in predicting and even prescribing employability. The review determined and analyzed the machine learning algorithms used in data mining to either predict or prescribe employability. This review used the PRISMA method to determine which studies from the existing literature to include as items for this SLR. Hence, 20 relevant studies, 16 of which are predicting employability and 4 of which are prescribing employability. These studies were selected from reliable databases: ScienceDirect, Springer, Wiley, IEEE Xplore, and Taylor and Francis. According to the results of this study, various data mining techniques can be used to predict and/or to prescribe employability. Furthermore, the variables/factors that predict and prescribe employability vary by country and the type of prediction or prescription conducted research. Nevertheless, all previous studies have relied more on skill as the main factor that predict and/or prescribe employability in developed countries and none studies have been conducted in unstable developing countries. Therefore, the need to conduct research on predicting or prescribing employability in such countries by trying to use contextual factors beyond skill as features.Item Predicting Employability of Congolese Information Technology Graduates Using Contextual Factors: Towards Sustainable Employability(Sustainability Journal, 2022) Mwendia, Simon N.; Mburu, Lucy W.; Mpia, Héritier N.Predicting employability in an unstable developing country requires the use of contextual factors as predictors and a suitable machine learning model capable of generalization. This study has discovered that parental financial stability, sociopolitical, relationship, academic, and strategic factors are the factors that can contextually predict the employability of information technology (IT) graduates in the democratic republic of Congo (DRC). A deep stacking predictive model was constructed using five different multilayer perceptron (MLP) sub models. The deep stacking model measured good performance (80% accuracy, 0.81 precision, 0.80 recall, 0.77 f1-score). All the individual models could not reach these performances with all the evaluation metrics used. Therefore, deep stacking was revealed to be the most suitable method for building a generalizable model to predict employability of IT graduates in the DRC. The authors estimate that the discovery of these contextual factors that predict IT graduates’ employability will help the DRC and other similar governments to develop strategies that mitigate unemployment, an important milestone to achievement of target 8.6 of the sustainable development goals.