Designing and delivering bioinformatics project-based learning in East Africa.

ABSTRACT

BACKGROUND: The Eastern Africa Network for Bioinformatics Training (EANBiT) has matured through continuous evaluation, feedback, and codesign. We highlight how the program has evolved to meet challenges and achieve its goals and how experiential learning through mini projects enhances the acquisition of skills and collaboration. We continued to learn and grow through honest feedback and evaluation of the program, trainers, and modules, enabling us to provide robust training even during the Coronavirus disease 2019 (COVID-19) pandemic, when we had to redesign the program due to restricted travel and in person group meetings. RESULTS: In response to the pandemic, we developed a program to maintain "residential" training experiences and benefits remotely. We had to answer the following questions What must change to still achieve the RT goals? What optimal platforms should be used? How would we manage connectivity and data challenges? How could we avoid online fatigue? Going virtual presented an opportunity to reflect on the essence and uniqueness of the program and its ability to meet the objective of strengthening bioinformatics skills among the cohorts of students using different delivery approaches. It allowed an increase in the number of participants. Evaluating each program component is critical for improvement, primarily when feedback feeds into the program's continuous amendment. Initially, the participants noted that there were too many modules, insufficient time, and a lack of hands-on training as a result of too much focus on theory. In the subsequent iterations, we reduced the number of modules from 27 to five, created a harmonized repository for the materials on GitHub, and introduced project-based learning through the mini projects. CONCLUSION: We demonstrate that implementing a program design through detailed monitoring and evaluation leads to success, especially when participants who are the best fit for the program are selected on an appropriate level of skills, motivation, and commitment.