TEACHING |
1 |
Know your Students |
This is an essential rule and applies to all teaching projects. However, it is particularly pertinent for this course where we have had a diverse set of student needs and levels of experience to support. By knowing students’ needs prior to delivering the course, educators can preempt topics and tailor support accordingly. |
2 |
Follow a pedagogy-first approach |
In technology-enhanced learning projects, the pedagogy can be overshadowed by the technology. This can be even more of an issue when the tools themselves are core subject matter of the course. By focusing on the teaching methods and ignoring the software and platforms at the start, educators can build the right foundations to ensure all the learning objectives are embedded effectively in the students’ learning journey. |
3 |
Incorporate commonly used tools |
Fundamental to the learning design was that the tools included in the course needed to be actively used by clinical bioinformaticians in practice. By adhering to an authentic and practice-driven experience during the course, the students are equipped to develop their skills post-course with the experience, insight, and confidence to be effective from day one. |
4 |
Create coding snippets to scaffold the learning |
The Jupyter Notebooks developed for the course contained short blocks of coding accompanied by instructional content that helped build the learning incrementally. By incorporating self-directed and interactive materials in a simulated safe environment, the students can practice, experiment, and hone their programming skills [5]. This safe-to-fail learning space is particularly critical for clinical bioinformaticians whose role is to apply their programming skills to genomic data to help inform patient diagnosis and care. |
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5 |
Design with flexibility in mind |
Teaching in the moment to ensure that learning goals are met and students are supported was a really important rule learnt from running this course. It was made possible from the robust design methods and trusted pedagogic frameworks the course was built on. By pivoting to support students during delivery, we could meet their needs during the remote emergency teaching of the pandemic. It is also a lesson that we will apply to our teaching in general. |
APPLIED PRACTICE |
6 |
Bring it back to practice |
Projects for the final coding assessment included work on real-world tools, such as VariantValidator [6]. By directly improving tools used by clinical bioinformaticians, the students could see the impact of their work, thereby increasing their motivation during and after the course. |
7 |
Build in real-world problems and methods |
The students provided their own programming challenges, which they worked on in the team activities. By sourcing real problems to tackle, the students were being prepared for professional practice. By incorporating these within a simulated environment, they were learning to apply their skills using the tools and methods used in practice. |
8 |
Simulate a community of practice (CoP) |
Clinical bioinformatics, like other professions, could be made more efficient through a strong collaborative network to streamline and share the development of resources used in practice. By equipping students with the necessary skills to develop their networks and to learn to work collaboratively from the beginning, this can occur after the course. |
THE TEAM |
9 |
Try a team-teaching approach |
Having a course team from mixed disciplines and backgrounds sparked various innovative approaches in this course. This is a rule that has transferred across various different courses and has worked well for the team. |
10 |
Facilitate the journey … well! |
Good facilitation is essential in all online delivery, so this rule is a fundamental one for online learning. This course recruited facilitators from the student community, provided pedagogic guidance, and ensured the facilitators were valued members of the team, which translated to the community feel of the course and encouraged engagement. |