Browsing by Author "Mayeku, Betty"

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Influence of social personalization on performance in group learning
(IEEE, 2017) Mayeku, Betty; Wabwoba, Franklin; Hogrefe, Dieter
Though personalization has been proposed as an approach that addresses learners' individual differences, the focus of studies in this domain has mainly been on tailoring learning to individual learners' needs as compared to group learning. The few studies that have factored the aspect of learner personality in group learning have overlooked learners' sociological preferences when modeling learner personality. However, it is of essence for learners' social personalities to be considered in group formation since social presence is the basis for collaborative learning. Furthermore, sociological preferences differ among learners and this contributes to individual differences. This paper explored the effect of taking into account the social personalities of a learner in creating collaborative groups. The study was based on the use of PECALE software prototype that enhances personalization and learner engagement through context awareness. The group performance was measured based on how long a group could take to solve a given collaborative task. The results showed that groups that had similar sociological preferences spent significantly less time solving a task than the groups that were formed with no consideration of sociological preferences. Assigning learners into groups while adhering to their sociological needs may offer a platform for equity and inclusion in collaborative learning since every learner's needs are addressed. This may in turn enhance group productivity. Furthermore, the study's approach may also be useful in formation of teams that can work effectively together in the workplace.
The role of AI in reducing maternal mortality: Current impacts and future potentials: Protocol for an analytical cross-sectional study
(PLoS One, 2025-05-14) Owoche, Patrick Oduor; Shisanya, Morris Senghor; Mayeku, Betty; Namusonge, Lucy Natecho
Background Maternal and newborn mortality remains a critical public health challenge, particularly in resource-limited settings. Despite global efforts, Kenya continues to report high maternal mortality rates of over 350 deaths per 100,000 live births and a neonatal mortality rate of 21 per 1,000 live births. Artificial Intelligence (AI)-enabled maternal healthcare interventions, such as Obstetric Point-of-Care Ultrasound (OPOCUS) and AI-driven SMS intervention on Promoting Mothers through Pregnancy and Postpartum (PROMPTS), offer innovative solutions to improve early detection, diagnosis, and maternal health-seeking behaviors. However, there is limited evidence on their usability, feasibility, and impact on maternal and neonatal outcomes. Objective This study aims to assess the implementation, user experiences, and impact of OPOCUS and PROMPTS on maternal and neonatal health outcomes in Kenya. Specifically, it evaluates their effectiveness in reducing maternal complications, improving antenatal and postnatal care utilization, and enhancing clinical decision-making while identifying potential barriers to adoption and scalability. Methods This mixed-methods, cross-sectional study will be conducted in ten counties in Kenya that have integrated AI-based maternal healthcare interventions. Quantitative data will be collected from health facility records, national health databases (KHIS), and structured surveys, while qualitative data will be gathered through key informant interviews (KIIs) with healthcare providers and policymakers, as well as focus group discussions (FGDs) with maternal health service users. Statistical analyses will include comparative pre- and post-AI implementation assessments, with thematic analysis for qualitative insights. Expected outcomes The study will generate empirical evidence on the feasibility, effectiveness, and barriers to AI integration in maternal health services. Findings will inform policy recommendations, enhance AI-assisted maternal healthcare design, and support the scaling of AI-driven interventions to improve maternal and neonatal health outcomes in Kenya and other low-resource settings. Conclusion AI-based maternal health interventions hold promise for reducing maternal mortality, improving diagnostic accuracy, and enhancing health-seeking behaviors. However, their success depends on user experiences, healthcare system readiness, and policy alignment. This study will provide critical insights for evidence-based scaling and policy integration of AI in maternal healthcare.
Use of low cost wireless communication technology for education in rural Kenya
(ACM, 2010-06-15) Mayeku, Betty; Kilwake, Juma; Bertarelli, Fabio
With the advent of mobile revolution, the emerging mobile technologies can support a broad range of learning activities on a variety of mobile devices, a concept referred to as mobile learning. The combination of wireless communication technology and mobile computing is resulting in rapid transformations of the educational world. This paper explores the use of low cost wireless technology in combination with other existing technologies to bring education to rural and pastoralist tribes in Kenya who would otherwise not have access to education.