Globally, ODL institutions experience mismatch between scalability of numbers and scalability of success rates. This study explored the scalability of success rates in open, distance e-learning as perceived by the learners within the Chain of Response Model. The primary aim of the study was to look at online learners’ success rate by focusing on two institutional factors drawn from the Model, namely: the learner’s study modules related challenges and support services. The results of an online survey of 180 undergraduate and postgraduate online learners of Egerton University, Kenya, showed: (a) the response rate of 16%; (b) a mixture of hardware, software and personal factors were identified as pre-requisites for e-learning success: (c) a number of mathematically-based modules were identified as risks to success in online studies; and (d) while the learners saw the learner support services as important they were less satisfied with their provision. The present study points to two broad areas that require further studies. First, qualitative look into specific challenges that learners face with respect to learner support service provisions, modules interactivity, and those identified as difficult to follow and thus posing risks to the learners’ success. Second, investigation into tutor-learner contacts with the view of identifying whether such contacts are reactive or proactive.

The purpose of this study was to find out the challenges facing Machine Learning (ML) software development and create a design architecture and a workflow for successful deployment. Despite the promise in ML technology, more than 80% of ML software projects never make it to production. As a result, majority of companies around the world with investments in ML software are making significant losses. Current studies show that data scientists and software engineers are concerned by the challenges involved in these systems such as: limited qualified and experienced ML software experts, lack of collaboration between experts from the two domains, lack of published literature in ML software development using established platforms such as Django Rest Framework, as well as existence of cloud software tools that are difficult use. Several attempts have been made to address these issues such as: Coming up with new software models and architectures, frameworks and design patterns. However, with the lack of a clear breakthrough in overcoming the challenges, this study proposes to investigate further into the conundrum with the view of proposing an ML software design architecture and a development workflow. In the end, the study gives a conclusion on how the remedies provided helps to meet the objectives of study.