School of Technology
Permanent URI for this collectionhttp://192.168.8.146:4000/handle/123456789/70
Browse
3 results
Search Results
Item A knowledge-as-a-service support framework for ambient learning in Kenya(13th IADIS International Conference Information Systems, 2020) Mburu, Lucy W.; Karanja, Richard; Nyaga, Simon M.Knowledge as a Service (KaaS) is a relatively new model, albeit one that is rapidly gaining popularity within cloud computing environments. Over the recent years, learners have experienced a constant need to access on demand knowledge that is fully aligned with the paradigm of cloud computing. This need stems from the knowledge that users will be able to access applications and the information therein on demand, without the restrictions that are usually imposed by time and space. The KaaS model terms knowledge as the understanding of information based on its relevance to a specific context and problem area, thus forming a valuable resource for the human decision-making process. As motivated by the global sustainable development goal of ensuring inclusive and equitable quality education to promote learning opportunities for all, this research has developed a framework that is hinged on KaaS and utilizes knowledge from ambient learning systems. The main aim is to provide a platform for disseminating and exploiting the available knowledge to aid the learning process and, thus, to improve the quality of education on the ambient learning system. The research further explores how collaborative effort can be used to form a knowledge network that allows access to heterogeneous sources of knowledge. The research outcomes will benefit knowledge consumers such as the developers of ambient learning systems.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.