Timetabling effective online labs

 

Challenge:

Data analysis labs can become more difficult with increasing separation from the corresponding taught portion of the course. Rather than addressing a new challenge, this case study looks at ways to capitalise on the potential to improve matters now that staff don't need to book time in computer labs and students don't need to travel to them after lectures. 

Solution:

Labs / tutorials were delievered immediately after brief (~45 minute) lectures. Specific sessions were scheduled for software setup at the start of the course, and for drop-in questions as the course progressed. Students felt more confident after performing analyses on their own, known devices, rather than on official university machines to which they only had temporary access.

Overview of case study

College: MVLS

Class size: Medium (26-100)

Level: MSc

Course title: Bioinformatics Using R for Biologists

Teaching setting: Practical work / online 

Suitable for online/distance learning: Yes

Technological competency: Basic (General computer literacy - though this module's content required programming knowledge)

Administrative support: General

Contact: John Cole & Kirsty Hacking (staff profiles)

What was done?

  • One lecture was followed by one tutorial each day, for 9 days (over 3 working weeks).
  • Lectures were kept under 45 mins and reflected the theory to be covered in the tutorials which followed immediately after, creating a coherent narrative.
  • Regular stops throughout the lecture provided a space to check-in with students and answer questions.
  • Tutorials were ~3 hours long to provide students time to work independently after the session, to catch up and incorporate their learning.
  • Catch-up drop-ins were scheduled in both the second week and the third.
  • Students were told to download the software ahead of the course, and the first session was entirely for onboarding issues for the various packages used. This ensured that tutorial sessions were not bottlenecked for some students by technical issues.
  • The tutorial instructions had set knowledge checks throughout, ensuring students engaged with the material on a deeper level.
  • The MVLS ECR (Early Career Researcher) Teaching Network* was used to find bioinformaticians with an interest in gaining teaching experience. As the course was offered to students on a variety of related Programmes, these ECRs chaired sessions with the students from their respective fields to help students contextualise bioinformatics within their particular discipline.

* The ECR Teaching Network can be contacted via Dr Claire Donald and includes staff from a variety of disciplines interested in teaching experience.

Evaluation and student feedback

  • All students enjoyed the course and none failed to achieve a passing grade. The use of Zoom for tutorials worked as a benefit in many ways and all students now have a functional programme on their computer that they know how to use (previously if only using campus computers this would not be the case).
  • Accessibility and digital equity were main considerations and need to be pre-emptively prepared (e.g. introducing students to high contrast themes, how to change font and font size, anticipating poor internet connection or computing power, etc).
  • It was difficult at times to identify if any students were struggling and completely avoiding asking questions. In future years we are planning to use a Moodle quiz to allow students to upload plots/answer questions for automatic feedback on their work and to allow us to see which students are not yet engaging for an earlier intervention for support.
  • Zoom sessions required a moderator to sit in the main area to allow students in if the lost access.

Benefits

Students

  • Students end up with the programme fully functional on their computer with scripts prepared for future analysis (projects etc).

Staff

  • Being able to have students share their screen on Zoom and for remote control to be used made it easier to help the students with any issues.
  • No longer restricted by computer cluster size.

Challenges

Students

  • Internet connectivity and time to complete the work.
  • Accessibility issues.
  • Digital equity considerations (e.g. computing power, internet stability, etc.).

Staff

  • Must have enough knowledgeable staff to assist the sessions.
  • Identifying struggling students in a timely fashion.