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|Title: ||Designing a course model for distance-based online bioinformatics training in Africa: The H3ABioNet experience|
|Authors: ||Gurwitz, Kim T.|
Fernandes, Pedro L.
Judge, David P.
Entfellner, Jean-Baka Domelevo
Guerfali, Fatma Z.
Alzohairy, Ahmed Mansour
Salifu, Samson Pandam
|Issue Date: ||5-Oct-2017|
|Publisher: ||Plos Computational Biology|
|Citation: ||Gurwitz KT, Aron S, Panji S, Maslamoney S, Fernandes PL, Judge DP, et al. (2017) Designing a course model for distance-based online bioinformatics training in Africa: The H3ABioNet experience. PLoS Comput Biol 13(10): e1005715. https://doi.org/10.1371/journal.pcbi.1005715|
|Abstract: ||Africa is not unique in its need for basic bioinformatics training for individuals from a diverse
range of academic backgrounds. However, particular logistical challenges in Africa, most
notably access to bioinformatics expertise and internet stability, must be addressed in order
to meet this need on the continent. H3ABioNet (www.h3abionet.org), the Pan African Bioinformatics
Network for H3Africa, has therefore developed an innovative, free-of-charge
ªIntroduction to Bioinformaticsº course, taking these challenges into account as part of its
educational efforts to provide on-site training and develop local expertise inside its network.
A multiple-delivery±mode learning model was selected for this 3-month course in order to
increase access to (mostly) African, expert bioinformatics trainers. The content of the
course was developed to include a range of fundamental bioinformatics topics at the introductory
level. For the first iteration of the course (2016), classrooms with a total of 364
enrolled participants were hosted at 20 institutions across 10 African countries. To ensure
that classroom success did not depend on stable internet, trainers pre-recorded their lectures,
and classrooms downloaded and watched these locally during biweekly contact sessions.
The trainers were available via video conferencing to take questions during contact sessions, as well as via online ªquestion and discussionº forums outside of contact session
time. This learning model, developed for a resource-limited setting, could easily be adapted
to other settings.|
|Description: ||An article published by PLoS Computational Biology, 13(10): e1005715. https://doi.org/10.1371/journal.pcbi.1005715|
|Appears in Collections:||College of Science|
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