S2E5 - From Lesson to Course Planning

Overview

Teaching: 10 min
Exercises: 10 min

Questions

• What are the steps to designing a course?

Slides

Learning outcomes

• Know all the steps to designing a course.

Delivery planning

• Is it part of an extended curriculum?
• Is the training a requirement, or optional career development?
• Format: workshop, seminar, lecture, online training or mix online/in-person?
• Timing: what is the content and depth of the training?
• Do you need to invite any other external experts?
• What sort of venue/equipment do you need for this format?

From session to course – defining the aim

• Combine the who, what and why requirements into a course aim
  • Who: write clear trainee specifications; e.g. undergraduate biologists with basic knowledge of Unix and R
  • What & why: describe what trainees will learn, and the benefit of that
• Consider the where and when requirements (i.e. the logistics)
  • How much can you do in the time available (including tests, feedback)
  • Resources limitations (space, equipment, assistants)

Chris Taylor – Earlham Institute

From learning outcomes to a course outline

• A well-written course aim will guide the generation of LOs

• LOs should then be instantiated as activities and quality checks
  • Learning activities: scripts, slides, exercises, tutorials, …
  • Assessment tools: creative activities, written tests, …
  • Feedback tools: observation, interaction, forms, …
• Learning activities (LAs) should be tightly-linked to quality checks
  • This is not ‘teaching to the test’ because it’s training not education
• When all the LOs are expanded, you have your course outline

Chris Taylor – Earlham Institute

Reproducibility of compute environments

• Different courses, different compute requirements: Unix, R, Python, metagenomics, long read sequencing
• Installation process time-consuming and technically challenging
• Every computer should have an identical installation setup and sufficient hardware (power and memory) to run the tools
• Virtual machines, cloud computing, containers, software images

Training rooms for bioinformatics

Physical environment 

• Room geometry: seats’ quality, the lighting, the room temperature control, the stability of power and network connections
• Functionality : video, audio, drawing surfaces (whiteboard, flipchart paper), a corkboard  to pin materials
• Hardware needs: power suppliers, network connections with a good quality wifi access

Training materials: sharing and making re-use possible

Learning outcome: Be able to identify training materials that exist already, and develop a routine of sharing training materials.

FAIR principles

Data and models are:

• Findable - can be searched for by the community after publication
• Accessible - can be read/downloaded by other researchers
• Interoperable - can be understood clearly in the context of the original experiment
• Re-usable - can be used by other researchers

FAIR principles – In the context of training

Training course materials: slides, exercises, datasets

• Findable - can be searched and found by the trainers community
• Accessible - can be read/downloaded by other trainers
• Interoperable - can be understood clearly in the context of the original course
• Re-usable - can be used by other trainers

Training materials repositories and resources

• GOBLET – http://mygoblet.org/training-portal
• TeSS - https://tess.elixir-europe.org/
• GitHub - https://github.com
• Jupyter - http://jupyter.org/
• Other?

Resources

Design: ftp://gtpb.igc.gulbenkian.pt/bicourses/posters/Calix_March2013.pdf Training materials: https://www.mygoblet.org/training-portal TeSS: https://tess.elixir-europe.org https://www.clinton.edu/curriculumcommittee/listofmeasurableverbs.cxml

Adopt collaborative platforms to support training activities

Challenge: Apply (5 min)

Articulate a goal of good teaching practice that you are ready to apply for your next training

Key Points

• Plan.

• Define aims.

• Reproducibility.

• Training environment and materials.