Ruth Wario

Prototyping mAgriculture applications among smallholder farmers

mAgriculture applications are gaining popularity in developing countries, with various services provided on the mobile platform. However, most of the applications do not get to the extent of widespread adoption, due to reasons that include but not limited to poor requirements gathering process that does not fully accommodate the intended user, poor design principles, development or deployments. In this research paper, we looked at the process of prototyping an mAgriculture application among smallholder farmers. The research involved 56 farmers from Kirinyaga and Embu counties in Kenya. The aim of this research was to evaluate the usage of an mAgriculture platform and to identify the factors that influence the usage of an mAgriculture application among smallholder farmers. The study identified various key factors that determine the usage of mAgriculture applications, as well as key considerations that should be considered for mAgriculture applications.

A Framework for Mobile Learning Technology Usability Testing

Mobile devices have gained popularity among learners prompting educators to adopt mobile for teaching and learning. Despite high use of mobile devices amongst learners, there is no direct relationship between device ownership and its usability. Educators demand that a technological learning tool must be usable and effective in supporting the way learners learn, however, there are few tools to evaluate usability of mobile devices within the context of learning. Using focus groups and learners as participants, this study, developed a framework for usability testing. Suitable Human Interactive Elements (HIE’s) were proposed by the focus group with their associated measurements to computer indexes. The framework was validated and used to evaluate iPad use in class at a tertiary institution. The results revealed that 88.1% of learners were satisfied learning with iPad with a correlation of .926 between learners’ and lecturers usability testing.

The feasibility of using an android-based infant fingerprint biometrics system for treatment follow-up

Lack of appropriate follow-up of HIV Exposed Infants (HEIs) after delivery contributes to suboptimal access and uptake of HIV Early Infant Diagnosis (EID) interventions. This could result in unidentified HEIs and potentially HIV-infected infants who are at risk of morbidity and mortality where interventions such as Anti-Retroviral Therapy (ART) are not commenced. Biometric identification of infants has been used successfully to increase immunization coverage. This study investigated the feasibility and acceptability of using biometrics for unique identification during EID follow-up of HEIs at the Kenyatta National Hospital (KNH) High-risk Clinic.

Towards Group Fuzzy Analytical Hierarchy Process

Group decision making takes place in almost all domains. In building construction domain, a team of contractors with disparate specializations collaborate. Little research has been done to propose group decision making technique for this domain. As such, specific teams’ competitiveness enhancements are minimal as it takes more time for individual evaluators to choose the right partners. Qualitative and quantitative methods were used. Themes and categorizations were based on deductive approach. Subsequently, Group Fuzzy Analytical Hierarchy Process (GFAHP), Multi-Criteria Decision Making (MCDM) algorithm, was designed and applied. It uses all evaluation criteria unlike Fuzzy AHP (FAHP) which excludes some criteria that are assigned zero weights. GFAHP reduces the number of pairwise comparisons required when a large number of attributes are to be compared. Validation of the technique carried out by five case studies, show that GFAHP is approximately 98.7% accurate in the selection of partners.

Designing adaptive learning support through machine learning techniques

The use of web 2.0 technologies in web based learning systems has made learning more learner-centered. In a learner centered environment, there is need to provide appropriate support to learners based on individual learner characteristics in order to maximize learning. This requires a Web-based learning system to have an adaptive interface to suit individual learner characteristics in order to accommodate diversity of learner needs and abilities and to maintain an appropriate context for interaction and for achieving personalized learning. The purpose of this paper is to discuss how machine learning techniques can provide adaptive learning support in a Web-based learning system. In this research, two machine learning algorithms namely: Heterogeneous Value Difference Metric (HVDM) and Naive Bayes Classifier (NBC) were used. HVDM was used to determine those learners who were similar to the current learner while NBC was used to estimate the likelihood that the learner would need to use additional materials for the current concept. To demonstrate the concept we used a course in object oriented programming (OOP).