The following are the top best practices that make the IBT 2017 a successful course in spite of all the reported challenges:
- Recruitment criteria: We note that graduates from certain faculties withdrew the course early on. It did help in yielding a high success rate that we had a long waiting list in place.
- Class diversity: in terms of academic backgrounds, graduating universities... etc. Proper advertising, and conscious selection of participants (in lights of the drop out factors we highlighted) should help in a better outcome.
- Registration database: We had so many people interested in the IBT, that at the time of writing, we have about 400 registered user in our database, coming from different universities, career status and educational levels. <I don't think we need to actually plot the demographics of these registered individuals, but we need to say more about them.>
- Sessions and Modules learning outcomes: At the beginning of each session in each module, trainers identify learning outcomes to the participants and they emphasize on them. This gives the participants a clear idea of what they should expect from each session. At the end of the course they were able to evaluate to what level the course content met their expectations. Their satisfaction with the course was a good indicator of the course success. This was clearly reflected by the percentage of the successful participants, whom were able to meet all the course requirements.
- Teaching assistants and staff training:
- Trainers interactivity sessions with course participants: Trainers were able to upload their session resources in the central panel of the Mconf interface should they have wanted to explain a concept on a particular lecture slide, for example. Trainers activated their webcams while answering questions.
- Hands-on sessions & Teaching assistants: During this free 3-month introduction to bioinformatics, IBT2017, a large portion of a course materials were dedicated to hands-on sessions, where participants are given the opportunity to practice what they are learning. Nevertheless, these sessions require a large number of teaching assistants, as they offer participants the opportunity to handle real data and run analysis tasks that implement the theory being illustrated in the lectures. This was found to be of great importance, as often trainees fail to appreciate how what is explained in the lectures can be directly applied to the data.
- Video Conferencing System (Mconf): IBT classrooms connected to the trainer and to other classrooms via the Mconf open-source web conferencing platform (http://mconf.org; in use here is the South African instance of Mconf https://mconf.sanren.ac.za/, hosted by South African National Research Network (SANREN) http://www.sanren.ac.za/south-african-nren/ \cite{Gurwitz_2017}. Classrooms either activated their microphones or entered text into a chat box to ask questions to the trainer.
- Learning Management Systems (Vula): Vula is the University of Cape Town’s (UCT) online learning and collaboration environment built on Sakai, which is a free, open-source educational software platform (https://sakaiproject.org). Vula was utilized in order to send out announcements, manage participants, track their progress, and allow for live or delayed interaction amongst participants and with trainers and staff \cite{Gurwitz_2017}.
- Local Classes Logistics and Planning: