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November 1st 2017, GetIN was rolled out in Kanungu District and to date the total number of young pregnant women reached by this initiative in Kanungu alone is 1000 women. They have been provided with information about access to Antenatal services, the benefits of attending ANC, and how to go about delivering at the hospital.
Since most women die due to unskilled maternal health care delivery services, GetIN believes that linking pregnant girls to health facilities for ANC, delivery and post par-tum care services will help reduce the high rate of maternal deaths in Uganda.
The GetIN team continues to support health workers in reaching out to young pregnant women by giving them access to a smart phone and the GetIN App. This enables the health workers to enter these women into the system so as to follow up and monitor them. The team also does technical visits to support health workers on any technology related issues on using the app.
On August 3rd 2018, GetIN was rolled out in Bundibugyo as a continuation of their successful pilot launch in Kanunugu district.
On Friday 17th August 2018 Mr. Godfrey Onyiat defended his doctoral thesis - A Model for Personalizing Learning in an E-Learning System supervised by Prof. Gilbert Maiga and Prof. Jude Lubega.
Prior to the paradigm shift to learner-centred learning, instructor-centred learning and mass education dominated the education system. This worked because the numbers of learners, their diversity and demands were closely related. With the increasing demand for higher education, diversity of learners and their varying learner needs, this approach is no longer satisfactory. This has caused dissatisfaction among the learners, leading to learner unrest, increasing learner drop-out and failure rates. Hence, a model for personalizing learning in an e-learning system is necessary to address the learner diversity, varying learner needs.
The study was set to come up with a model for personalizing learning in an e-learning system. This was done by identifying the factors for institutional readiness for e-learning, the requirements for personalized learning, propose a model that supports personalizing learning, and to evaluate it. A survey was conducted to gather requirements for the model using questionnaire and interview methods. Four hundred (400) second and third year Information Technology students and their corresponding lecturers were purposively selected from both public and private universities in Uganda. The data was analysed using SPSS and the results were used to extend the model for personalizing learning. Seventy (70) Information Technology Instructors from the study sample evaluated the model using a questionnaire and expert opinions to establish the relationship between personalized learning and institutional readiness for e-learning; and its applicability in the real world.
The results indicate that the extended model is suitable for personalizing learning in an e-learning system. The study identified two (2) themes for personalizing learning in an e-learning system: institutional readiness for e-learning and personalized learning. The following factors were established for determining personalized learning: commitment, motivation, engagement, and experience; while for institutional readiness for e-learning are: awareness, institutional culture, management support, feedback, technology, human resource, and reliability. These are linked to both improved e-learning implementation and successful personalized learning. From a practical point of view, it provides a generic model that can guide practitioners in personalizing and implementing e-learning.
After close scrutiny of his research and addressing several open-ended questions, the examination committee that comprised of Assoc. Prof. Rwashana, Dr. Agaba, Dr. Kivunike, Dr. Kahiigi, Dr. Bagarukayo and Assoc. Prof. Owiny accepted Mr. Onyiat’s research with major revisions in content and format (with one committee member responsible for overseeing and approving the major revisions before the final copies are submitted).
On the 6th August 2018 Mr. Peter Ochieng defended his doctoral thesis - A Large Scale Ontology Matching Tool Based on a Statistical Predictive Model. He was supervised by Dr. Kyanda Swaib Kawaase and Dr. Johnson Mwebaze.
Ontologies have become more pervasive in Computer Science and especially in the semantic web. They provide the consensual formal vocabulary to be shared between applications. Through ontologies, new generations of semantic applications such as semantic search, semantic portal, intelligent advisory systems, semantic middleware and semantic software engineering techniques have been developed. However, due to the decentralized nature of ontologies development, ontologies within a given domain naturally become heterogeneous hence limiting semantic integration of different applications. In this thesis, we implement a statistically based ontology matching system which effectively aligns two large heterogeneous ontologies.
We integrate techniques in the ontology matching tool such that the space and time complexities challenge associated with large ontologies matching can be effectively minimized. By surveying the existing ontology matching tools and approaches, we identified a number of research gaps which limit their effectiveness during large ontology matching. Key among the challenges identified is the lack of adequate techniques to address the high space and time complexities associated with matching large ontologies. In order to reduce space complexity, this thesis implements an ontology partitioning technique using spectral clustering.
To address the challenge of time complexity, we use the Single Instruction Multiple Data parallelization technique (SIMD). We finally implement a tag-based alignment repair technique to ensure high quality mappings. In summary this thesis implements techniques that reduce the space, time complexity and ensure quality of entity mappings of two large ontologies.
The examination committee that comprised Assoc. Prof. Maiga, Dr. Zawedde, Dr. Mirembe, Dr. Nabende, Dr. Mwebaze, Dr. Nakakawa and Dr. Balikudembe engaged with Mr. Ochieng and asked several questions regarding his research and his thesis. The committee then had a closed-door session to deliberate over Mr. Ochieng defense that was then accepted with minor revisions (no further approval required).