Qualitative Data Analysis Supervision in Higher Education: Towards Atlas.ti Framework in Sub-Saharan Distance Education

Introduction Sub-Saharan Africa faces critical challenges in distance education research supervision, particularly for qualitative data analysis using CAQDAS tools like Atlas.ti. Many supervisors rely on manual coding methods due to technology adoption barriers, creating inadequate student support. These challenges are compounded by infrastructure limitations, cultural preferences for face-to-face interaction, and limited digital literacy. Resulting impacts include extended completion times, poor analysis quality, and high dropout rates, hindering achievement of SDG 4 and South Africa's NDP PhD targets by 2030.

Goals and Methods This study will examine academic supervisor's challenges in distance qualitative data analysis supervision across Sub-Saharan Africa and develop al framework for capacitation. The meta-analysis design will analyse peer-reviewed literature from 2015-2024. Data collection will involve systematic searches of Scopus, Web of Science, PubMed, ERIC, and JSTOR, supplemented by citation tracking. Inclusion criteria will focus on qualitative data analysis supervision, distance education challenges, and CAQDAS adoption in Sub-Saharan contexts. The conceptual framework applies Technology Acceptance Model, Diffusion of Innovation theory, and fifth-generation distance education model to synthesise findings. The framework will further integrate academic literature with theoretical foundations, incorporating cultural responsiveness and asynchronous supervision protocols.

Expected Results Preliminary findings indicate significant challenges in infrastructure reliability and supervisor comfort with Atlas.ti tools. The framework will address these through multiple phases: digital readiness assessment, asynchronous scaffolding, cultural integration with Ubuntu philosophy, flexible technology adoption, and community-based quality assurance. Initial testing suggests potential for substantial reduction in technology barriers and improved completion rates. The study will contribute evidence-based recommendations for integrating Atlas.ti supervision whilst respecting cultural constraints.

Conclusions This research will contribute to a comprehensive framework addressing qualitative data analysis supervision in Sub-Saharan distance education. By combining technological innovation with cultural sensitivity, the framework promises to enhance supervision quality, reduce completion delays, and support regional PhD production rates.