The Future of Technology-Based Learning at the Open University of Tanzania

Today, digital transformation in higher education reshapes traditional educational systems toward technology-based learning. In the wake of the global pandemic COVID-19, digital transformation has even accelerated at many universities worldwide due to the pressure put on policymakers and university management to adopt educational technology at their institutions to allow education to continue. Using the case of the Open University of Tanzania (OUT), this article discusses critical factors needed for the successful implementation of technology-based learning and other technological innovations like adaptive learning, for example, in higher education in an African context. We applied a Delphi design, a rigorous research method used for structuring a group communication process to allow a group of experts, as a whole, to deal with a complex problem effectively. In total, 24 experts (e.g., instructors, staff, and students) from different regional OUT centres participated in the Delphi study. The paper presents the results of the first round of the Delphi study on the challenges of technologybased learning identified at OUT providing the first insights into the perceived role, probability, and estimated realisation time of adaptive learning at OUT in the future. We argue that not only technological challenges linked to the internet, network, or technological equipment affect the adoption of technology-based learning in higher education, but also that pedagogical, organisational, and global challenges are indispensable for the successful transformation of higher education. © 2022. International Journal of Emerging Technologies in Learning. All Rights Reserved.

A Machine Learning Resource Allocation Solution to Improve Video Quality in Remote Education

The current global pandemic crisis has unquestionably disrupted the higher education sector, forcing educational institutions to rapidly embrace technology-enhanced learning. However, the COVID-19 containment measures that forced people to work or stay at home, have determined a significant increase in the Internet traffic that puts tremendous pressure on the underlying network infrastructure. This affects negatively content delivery and consequently user perceived quality, especially for video-based services. Focusing on this problem, this paper proposes a machine learning-based resource allocation solution that improves the quality of video services for increased number of viewers. The solution is deployed and tested in an educational context, demonstrating its benefit in terms of major quality of service parameters for various video content, in comparison with existing state of the art. Moreover, a discussion on how the technology is helping to mitigate the effects of massively increasing Internet traffic on the video quality in an educational context is also presented. IEEE